Ecology

Field-data collectionFieldwork was conducted in DRC between January 2018 and March 2020. Ten transects (4–11 km long) were installed, identical to the approach in ref. 9, in locations that were highly likely to be peatland. These were selected to help test hypotheses about the role of vegetation, surface wetness, nutrient status and topography in peat accumulation (Fig. 1a and Supplementary Table 1). A further eight transects (0.5–3 km long) were installed to assess our peat mapping capabilities (Fig. 1a and Supplementary Table 1).Every 250 m along each transect, land cover was classified as one of six classes: water, savannah, terra firme forest, non-peat-forming seasonally inundated forest, hardwood-dominated peat swamp forest or palm-dominated peat swamp forest. Peat swamp forest was classified as palm dominated when >50% of the canopy, estimated by eye, was palms (commonly Raphia laurentii or Raphia sese). In addition, several ground-truth points were collected at locations in the vicinity of each transect from the clearly identifiable land-cover classes water, savannah and terra firme forest.Peat presence/absence was recorded every 250 m along all transects, and peat thickness (if present) was measured by inserting metal poles into the ground until the poles were prevented from going any further by the underlying mineral layer, identical to the pole method of ref. 9. In addition, a core of the full peat profile was extracted every kilometre along the ten hypothesis-testing transects, if peat was present, with a Russian-type corer (52 mm stainless steel Eijkelkamp model); these 63 cores were sealed in plastic for laboratory analysis.Peat-thickness laboratory measurementsPeat was defined as having an organic matter (OM) content of ≥65% and a thickness of ≥0.3 m (sensu ref. 9). Therefore, down-core OM content of all 63 cores was analysed to measure peat thickness. The organic matter content of each 0.1-m-thick peat sample was estimated via loss on ignition (LOI), whereby samples were heated at 550 °C for 4 h. The mass fraction lost after heating was used as an estimate of total OM content (% of mass). Peat thickness was defined as the deepest 0.1 m with OM ≥ 65%, after which there is a transition to mineral soil. Samples below this depth were excluded from further analysis. Rare mineral intrusions into the peat layer above this depth, where OM 4× the mean Cook’s distance were excluded as influential outliers. Mean pole-method offset was significantly higher along the DRC transects (0.94 m) than along those in ROC (0.48 m; P  More

Wanger, T. C. et al. Integrating agroecological production in a robust post-2020 Global Biodiversity Framework. Nat. Ecol. Evol. 4, 1150–1152 (2020).PubMed 
Article 

Google Scholar 
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).CAS 
PubMed 
Article 

Google Scholar 
Amundson, R. et al. Soil and human security in the 21st century. Science 348, 1261071 (2015).PubMed 
Article 
CAS 

Google Scholar 
Robertson, G. P. & Vitousek, P. M. Nitrogen in agriculture: balancing the cost of an essential resource. Annu. Rev. Environ. Resour. 34, 97–125 (2009).Article 

Google Scholar 
Campbell, B. M. et al. Agriculture production as a major driver of the Earth system exceeding planetary boundaries. Ecol. Soc. 22, 8 (2017).Article 

Google Scholar 
Kremen, C. & Merenlender, A. M. Landscapes that work for biodiversity and people. Science 362, eaau6020 (2018).PubMed 
Article 
CAS 

Google Scholar 
Krebs, A. V. The Corporate Reapers: The Book of Agribusiness (Essential Books, 1992).Mortensen, D. A. & Smith, R. G. Confronting barriers to cropping system diversification. Front. Sustain. Food Syst. 4, 564197 (2020).Article 

Google Scholar 
2017 Census of Agriculture – 2019 Organic Survey (USDA NASS, 2020); https://www.nass.usda.gov/Publications/AgCensus/2017/index.phpFarms and Land in Farms 2019 Summary (USDA NASS, 2020); https://usda.library.cornell.edu/concern/publications/5712m6524Reganold, J. P. & Wachter, J. M. Organic agriculture in the twenty-first century. Nat. Plants 2, 15221 (2016).PubMed 
Article 

Google Scholar 
Muller, A. et al. Strategies for feeding the world more sustainably with organic agriculture. Nat. Commun. 8, 1290 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lori, M., Symnaczik, S., Mäder, P., De Deyn, G. & Gattinger, A. Organic farming enhances soil microbial abundance and activity—a meta-analysis and meta-regression. PLoS ONE 12, e0180442 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Seufert, V. & Ramankutty, N. Many shades of gray—the context-dependent performance of organic agriculture. Sci. Adv. 3, e1602638 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
USDA AMS. National Organic Program; Final Rule, 7 CFR Part 205. Fed. Regist. 65, 80547–80684 (2000).
Google Scholar 
Wezel, A. et al. Agroecology as a science, a movement and a practice. A review. Agron. Sustain. Dev. 29, 503–515 (2009).Article 

Google Scholar 
Tamburini, G. et al. Agricultural diversification promotes multiple ecosystem services without compromising yield. Sci. Adv. 6, eaba1715 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Kleijn, D. et al. Ecological intensification: bridging the gap between science and practice. Trends Ecol. Evol. 34, 154–166 (2019).PubMed 
Article 

Google Scholar 
Bommarco, R., Kleijn, D. & Potts, S. G. Ecological intensification: harnessing ecosystem services for food security. Trends Ecol. Evol. 28, 230–238 (2013).PubMed 
Article 

Google Scholar 
Kremen, C. & Miles, A. Ecosystem services in biologically diversified versus conventional farming systems: benefits, externalities, and trade-offs. Ecol. Soc. 17, 40 (2012).
Google Scholar 
Bowles, T. M. et al. Long-term evidence shows that crop-rotation diversification increases agricultural resilience to adverse growing conditions in North America. One Earth 2, 284–293 (2020).Article 

Google Scholar 
Wood, S. A. et al. Functional traits in agriculture: agrobiodiversity and ecosystem services. Trends Ecol. Evol. 30, 531–539 (2015).PubMed 
Article 

Google Scholar 
Faucon, M.-P., Houben, D. & Lambers, H. Plant functional traits: soil and ecosystem services. Trends Plant Sci. 22, 385–394 (2017).CAS 
PubMed 
Article 

Google Scholar 
D’Hose, T. et al. The positive relationship between soil quality and crop production: a case study on the effect of farm compost application. Appl. Soil Ecol. 75, 189–198 (2014).Article 

Google Scholar 
Fließbach, A., Oberholzer, H.-R., Gunst, L. & Mäder, P. Soil organic matter and biological soil quality indicators after 21 years of organic and conventional farming. Agric. Ecosyst. Environ. 118, 273–284 (2007).Article 

Google Scholar 
Francioli, D. et al. Mineral vs. organic amendments: microbial community structure, activity and abundance of agriculturally relevant microbes are driven by long-term fertilization strategies. Front. Microbiol. 7, 1446 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Nunes, M. R., Karlen, D. L., Veum, K. S., Moorman, T. B. & Cambardella, C. A. Biological soil health indicators respond to tillage intensity: a US meta-analysis. Geoderma 369, 114335 (2020).CAS 
Article 

Google Scholar 
Blanco-Canqui, H. & Ruis, S. J. No-tillage and soil physical environment. Geoderma 326, 164–200 (2018).Article 

Google Scholar 
Willekens, K., Vandecasteele, B., Buchan, D. & De Neve, S. Soil quality is positively affected by reduced tillage and compost in an intensive vegetable cropping system. Appl. Soil Ecol. 82, 61–71 (2014).Article 

Google Scholar 
Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Albrecht, M. et al. The effectiveness of flower strips and hedgerows on pest control, pollination services and crop yield: a quantitative synthesis. Ecol. Lett. 23, 1488–1498 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Chaplin-Kramer, R., de Valpine, P., Mills, N. J. & Kremen, C. Detecting pest control services across spatial and temporal scales. Agric. Ecosyst. Environ. 181, 206–212 (2013).Article 

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

Google Scholar 
Karp, D. S. et al. Crop pests and predators exhibit inconsistent responses to surrounding landscape composition. Proc. Natl Acad. Sci. USA 115, E7863–E7870 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, X., Liu, X., Zhang, M., Dahlgren, R. A. & Eitzel, M. A review of vegetated buffers and a meta-analysis of their mitigation efficacy in reducing nonpoint source pollution. J. Environ. Qual. 39, 76–84 (2010).CAS 
PubMed 
Article 

Google Scholar 
Eyhorn, F. et al. Sustainability in global agriculture driven by organic farming. Nat. Sustain. 2, 253–255 (2019).Article 

Google Scholar 
Buck, D., Getz, C. & Guthman, J. From farm to table: the organic vegetable commodity chain of northern California. Sociol. Rural. 37, 3–20 (1997).Article 

Google Scholar 
Guthman, J. Raising organic: an agro-ecological assessment of grower practices in California. Agric. Hum. Values 17, 257–266 (2000).Article 

Google Scholar 
Guthman, J. The trouble with ‘organic lite’ in California: a rejoinder to the ‘conventionalisation’ debate. Sociol. Rural. 44, 301–316 (2004).Article 

Google Scholar 
Darnhofer, I., Lindenthal, T., Bartel-Kratochvil, R. & Zollitsch, W. Conventionalisation of organic farming practices: from structural criteria towards an assessment based on organic principles. A review. Agron. Sustain. Dev. 30, 67–81 (2010).Article 

Google Scholar 
Constance, D. H., Choi, J. Y. & Lyke-Ho-Gland, H. Conventionalization, bifurcation, and quality of life: certified and non-certified organic farmers in Texas. J. Rural Soc. Sci. 23, 208–234 (2008).
Google Scholar 
2017 Census of Agriculture – United States Summary and State Data (USDA NASS, 2019); https://www.nass.usda.gov/Publications/AgCensus/2017/index.php2017 Census of Agriculture: Characteristics of All Farms and Farms with Organic Sales (USDA NASS, 2019); https://www.nass.usda.gov/Publications/AgCensus/2017/index.phpPonisio, L. C. et al. Diversification practices reduce organic to conventional yield gap. Proc. R. Soc. B 282, 20141396 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Wezel, A. et al. Agroecological practices for sustainable agriculture. A review. Agron. Sustain. Dev. 34, 1–20 (2014).Article 

Google Scholar 
Gomiero, T., Pimentel, D. & Paoletti, M. G. Environmental impact of different agricultural management practices: conventional vs. organic agriculture. Crit. Rev. Plant Sci. 30, 95–124 (2011).Article 

Google Scholar 
Tittonell, P. et al. Agroecology in large scale farming—a research agenda. Front. Sustain. Food Syst. 4, 584605 (2020).Article 

Google Scholar 
Haan, N. L., Zhang, Y. & Landis, D. A. Predicting landscape configuration effects on agricultural pest suppression. Trends Ecol. Evol. 35, 175–186 (2020).PubMed 
Article 

Google Scholar 
Martin, E. A., Seo, B., Park, C.-R., Reineking, B. & Steffan-Dewenter, I. Scale-dependent effects of landscape composition and configuration on natural enemy diversity, crop herbivory, and yields. Ecol. Appl. 26, 448–462 (2016).PubMed 
Article 

Google Scholar 
Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes – eight hypotheses. Biol. Rev. 87, 661–685 (2012).PubMed 
Article 

Google Scholar 
Olimpi, E. M. et al. Evolving food safety pressures in California’s central coast region. Front. Sustain. Food Syst. 3, 102 (2019).Article 

Google Scholar 
Karp, D. S. et al. The unintended ecological and social impacts of food safety regulations in California’s central coast region. BioScience 65, 1173–1183 (2015).Article 

Google Scholar 
Bovay, J., Ferrier, P. & Zhen, C. Estimated Costs for Fruit and Vegetable Producers To Comply With the Food Safety Modernization Act’s Produce Rule, EIB-195 (U.S. Department of Agriculture, Economic Research Service, 2018).Coombes, B. & Campbell, H. Dependent reproduction of alternative modes of agriculture: organic farming in New Zealand. Sociol. Rural. 38, 127–145 (1998).Article 

Google Scholar 
Hughner, R. S., McDonagh, P., Prothero, A., Shultz, C. J. & Stanton, J. Who are organic food consumers? A compilation and review of why people purchase organic food. J. Consum. Behav. 6, 94–110 (2007).Article 

Google Scholar 
Smith, E. & Marsden, T. Exploring the ‘limits to growth’ in UK organics: beyond the statistical image. J. Rural Stud. 20, 345–357 (2004).Article 

Google Scholar 
Howard, P. H. Concentration and Power in the Food System: Who Controls What We Eat? (Bloomsbury, 2016).Arcuri, A. The transformation of organic regulation: the ambiguous effects of publicization. Regul. Gov. 9, 144–159 (2015).Article 

Google Scholar 
Seufert, V., Ramankutty, N. & Mayerhofer, T. What is this thing called organic? – How organic farming is codified in regulations. Food Policy 68, 10–20 (2017).Article 

Google Scholar 
Guthman, J. in Alternative Food Politics: From the Margins to the Mainstream (eds. Phillipov, M. & Kirkwood, K.) 23–36 (Routledge, 2019).Jaffee, D. & Howard, P. H. Corporate cooptation of organic and fair trade standards. Agric. Hum. Values 27, 387–399 (2010).Article 

Google Scholar 
Campbell, H. & Rosin, C. After the ‘organic industrial complex’: an ontological expedition through commercial organic agriculture in New Zealand. J. Rural Stud. 27, 350–361 (2011).Article 

Google Scholar 
Lockie, S. & Halpin, D. The ‘conventionalisation’ thesis reconsidered: structural and ideological transformation of Australian organic agriculture. Sociol. Rural. 45, 284–307 (2005).Article 

Google Scholar 
Prokopy, L. S. et al. Adoption of agricultural conservation practices in the United States: evidence from 35 years of quantitative literature. J. Soil Water Conserv. 74, 520–534 (2019).Article 

Google Scholar 
Pretty, J. et al. Global assessment of agricultural system redesign for sustainable intensification. Nat. Sustain. 1, 441–446 (2018).Article 

Google Scholar 
Gliessman, S. Transforming food systems with agroecology. Agroecol. Sustain. Food Syst. 40, 187–189 (2016).Article 

Google Scholar 
Hill, S. B. Redesigning the food system for sustainability. Alternatives 12, 32–36 (1985).
Google Scholar 
Padel, S., Levidow, L. & Pearce, B. UK farmers’ transition pathways towards agroecological farm redesign: evaluating explanatory models. Agroecol. Sustain. Food Syst. 44, 139–163 (2020).Article 

Google Scholar 
Esquivel, K. E. et al. The ‘sweet spot’ in the middle: why do mid-scale farms adopt diversification practices at higher rates? Front. Sustain. Food Syst. 5, 734088 (2021).Article 

Google Scholar 
Brislen, L. Meeting in the middle: scaling-up and scaling-over in alternative food networks. Cult. Agric. Food Environ. 40, 105–113 (2018).Article 

Google Scholar 
De Master, K. New inquiries into the agri-cultures of the middle. Cult. Agric. Food Environ. 40, 130–135 (2018).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 
CAS 

Google Scholar 
Lenth, R. V. emmeans: Estimated marginal means, aka least-squares means. R package version 1.7.4-1 https://CRAN.R-project.org/package=emmeans (2021).Wasserstein, R. L. & Lazar, N. A. The ASA statement on p-values: context, process, and purpose. Am. Stat. 70, 129–133 (2016).Article 

Google Scholar 
Krueger, J. I. & Heck, P. R. Putting the P-value in its place. Am. Stat. 73, 122–128 (2019).Article 

Google Scholar 
Wasserstein, R. L., Schirm, A. L. & Lazar, N. A. Moving to a world beyond ‘p < 0.05’. Am. Stat. 73(Suppl. 1), 1–19 (2019).Article  Google Scholar  Agresti, A. Categorical Data Analysis (Wiley, 2013). More

Our long-term surveillance of SARS-CoV-2 in Austria demonstrated that WBE alone yields a time-resolved map of the genetic dynamics during a pandemic. Yet one task of pathogenomic surveillance is to link genetic pathogen information with clinical manifestation and the immunological status of patients. WBE is limited in that regard since the available data are anonymized to start with. Nonetheless, WBE provides invaluable population-level guidance on epidemiological developments, which complements case-based surveillance and provides information for optimal resource allocation. This notion can also be transferred to a global perspective. WBE provides a tool to shed light on blind spots of pathogen surveillance in places and communities with poor healthcare accessibility. If carefully set up and used in respectful and coequal terms, WBE of infectious diseases could make an important contribution to global safety.To this end, several challenges must be overcome. Current WBE methods need to be expanded to other pathogens beyond SARS-CoV-2 and validated with case-based epidemiological data. Furthermore, current methods must be adapted and optimized to be applicable in locations without a centralized sewer infrastructure5. Finally, international sharing of wastewater-based pathogen sequencing data will be needed to unleash the full potential of WBE for global pathogen surveillance.We are confident that our study will support initiatives already working in these directions, as well as encouraging intensified efforts to exploit such population-level surveillance approaches in the global fight against infectious diseases.
Fabian Amman
1
& Andreas Bergthaler
2

1
CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria

2
Medical University Vienna, Vienna, Austria More

Snyder-Mackler, N. et al. Science 368, eaax9553 (2020).CAS 
Article 

Google Scholar 
Wrzus, C., Hänel, M., Wagner, J. & Neyer, F. J. Psychol. Bull. 139, 53–80 (2013).Article 

Google Scholar 
Steptoe, A., Shankar, A., Demakakos, P. & Wardle, J. Proc. Natl Acad. Sci. USA 110, 5797–5801 (2013).CAS 
Article 

Google Scholar 
Almeling, L., Hammerschmidt, K., Sennhenn-Reulen, H., Freund, A. M. & Fischer, J. Curr. Biol. 26, 1744–1749 (2016).CAS 
Article 

Google Scholar 
Rosati, A. G. et al. Science 370, 473–476 (2020).CAS 
Article 

Google Scholar 
Schino, G. & Pinzaglia, M. Am. J. Primatol. 80, e22746–e22747 (2018).Article 

Google Scholar 
Machanda, Z. P. & Rosati, A. G. Phil. Trans. R. Soc. Lond. B 375, 20190620 (2020).Article 

Google Scholar 
Kroeger, S. B., Blumstein, D. T. & Martin, J. G. A. Phil. Trans. R. Soc. Lond. B 376, 20190745 (2021).Article 

Google Scholar 
Weiss, M. N. et al. Proc. R. Soc. Lond. B 288, 20210617 (2021).
Google Scholar 
Albery, G. F. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01817-9 (2022).Article 
PubMed 

Google Scholar 
Siracusa, E. R., Higham, J. P., Snyder-Mackler, N. & Brent, L. J. N. Biol. Lett. 18, 20210643 (2022).Article 

Google Scholar 
Nussey, D. H., Coulson, T., Festa-Bianchet, M. & Gaillard, J. M. Funct. Ecol. 22, 393–406 (2008).Article 

Google Scholar 
Nussey, D. H., Froy, H., Lemaître, J.-F., Gaillard, J.-M. & Austad, S. N. Ageing Res. Rev. 12, 214–225 (2013).Article 

Google Scholar  More

Potts, S. et al. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).Article 

Google Scholar 
Thomann, M., Imbert, E., Devaux, C. & Cheptou, P.-O. Flowering plants under global pollinator decline. Trends Plant Sci. 18, 353–359 (2013).CAS 
Article 

Google Scholar 
Pauw, A. Can pollination niches facilitate plant coexistence? Trends Ecol. Evol. 28, 30–37 (2013).Article 

Google Scholar 
Johnson, C. A. How mutualisms influence the coexistence of competing species. Ecology 102, e03346 (2021).PubMed 

Google Scholar 
Tilman, D. Resource Competition and Community Structure (Princeton Univ. Press, 1982).Tilman, D. Constraints and tradeoffs: toward a predictive theory of competition and succession. Oikos 58, 3–15 (1990).Article 

Google Scholar 
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–358 (2000).Article 

Google Scholar 
Mitchell, R. J., Flanagan, R. J., Brown, B. J., Waser, N. M. & Karron, J. D. New frontiers in competition for pollination. Ann. Bot. 103, 1403–1413 (2009).Article 

Google Scholar 
Morales, C. L. & Traveset, A. A meta-analysis of impacts of alien vs. native plants on pollinator visitation and reproductive success of co-flowering native plants. Ecol. Lett. 12, 716–728 (2009).Article 

Google Scholar 
Jones, E. I., Bronstein, J. L. & Ferrière, R. The fundamental role of competition in the ecology and evolution of mutualisms. Ann. N. Y. Acad. Sci. 1256, 66–88 (2012).ADS 
Article 

Google Scholar 
Memmott, J., Waser, N. M. & Price, M. V. Tolerance of pollination networks to species extinctions. Proc. R. Soc. Lond. B 271, 2605–2611 (2004).Article 

Google Scholar 
Bascompte, J. & Jordano, P. Mutualistic Networks (Princeton University Press, 2013).Bascompte, J. Mutualism and biodiversity. Curr. Biol. 29, R467–R470 (2019).CAS 
Article 

Google Scholar 
Chesson, P. Updates on mechanisms of maintenance of species diversity. J. Ecol. 106, 1773–1794 (2018).Article 

Google Scholar 
Levin, D. A. & Anderson, W. W. Competition for pollinators between simultaneously flowering species. Am. Nat. 104, 455–467 (1970).Article 

Google Scholar 
Kunin, W. & Iwasa, Y. Pollinator foraging strategies in mixed floral arrays: density effects and floral constancy. Theor. Popul. Biol. 49, 232–263 (1996).CAS 
Article 

Google Scholar 
Lanuza, J. B., Bartomeus, I. & Godoy, O. Opposing effects of floral visitors and soil conditions on the determinants of competitive outcomes maintain species diversity in heterogeneous landscapes. Ecol. Lett. 21, 865–874 (2018).Article 

Google Scholar 
Thomson, J. Spatial and temporal components of resource assessment by flower-feeding insects. J. Anim. Ecol. 50, 49–59 (1981).Article 

Google Scholar 
Knight, T. M. et al. Reflections on, and visions for, the changing field of pollination ecology. Ecol. Lett. 21, 1282–1295 (2018).MathSciNet 
CAS 
Article 

Google Scholar 
Biella, P. et al. Experimental loss of generalist plants reveals alterations in plant-pollinator interactions and a constrained flexibility of foraging. Sci. Rep. 9, 7376 (2019).ADS 
Article 

Google Scholar 
Brosi, B. & Briggs, H. M. Single pollinator species losses reduce floral fidelity and plant reproductive function. Proc. Natl Acad. Sci. USA 110, 13044–13048 (2013).ADS 
CAS 
Article 

Google Scholar 
Addicott, J. F. in The Biology of Mutualism (ed. Boucher, D. H.) 217–247 (Croom Helm, 1985).Knight, T. M. et al. Pollen limitation of plant reproduction: pattern and process. Annu. Rev. Ecol. Evol. Syst. 36, 467–497 (2005).Article 

Google Scholar 
Bartomeus, I., Saavedra, S., Rohr, R. P. & Godoy, O. Experimental evidence of the importance of multitrophic structure for species persistence. Proc. Natl Acad. Sci. USA 118, e2023872118 (2021).CAS 
Article 

Google Scholar 
Levine, J. M., Bascompte, J., Adler, P. B. & Allesina, S. Beyond pairwise mechanisms of species coexistence in complex communities. Nature 546, 56–64 (2017).ADS 
CAS 
Article 

Google Scholar 
Saavedra, S. et al. A structural approach for understanding multispecies coexistence. Ecol. Monogr. 87, 470–486 (2017).Article 

Google Scholar 
Rinella, M. J., Strong, D. J. & Vermeire, L. T. Omitted variable bias in studies of plant interactions. Ecology 101, e03020 (2020).Article 

Google Scholar  More

Study sites and swarm characterizationThe survey was conducted in 10 villages in south-western Burkina Faso especially around the district of Bobo-Dioulasso, Santitougou (N11° 17′ 16″, W4° 13′ 04″), Kimidougou (N11° 17′ 53″; W4° 14′ 11″), Nastenga (N10.96871; W003.23477), Zeyama (N10.87638; W 003.26145), Mogobasso (N11° 25′ 31″, W4° 06′ 08″), Synbekuy (N11° 53′ 28″, W3° 44′ 02″), Ramatoulaye (N11° 33′ 39″, W3° 57′ 05″) Syndombokuy (N11° 53′ 06″, W3° 43′ 19″), Lampa (N11.16464; W 003.6374) et Syndounkuy (N11.14541; W 003.05141) (Fig. 1). All villages are located north of Bobo-Dioulasso, on the national road 10 (N10), ranged from 20 and 90 km. The region is characterised by wooded savannah located in south-western Burkina Faso, and the mean annual rainfall is about 1200 mm. The rainy season extends from May to October and the dry season from November to April. Malaria transmission in the area extends from June to November. However, residual transmission may occur beyond this period in specific locations. An. gambiae is the major malaria vector following by An. coluzzii and An. Arabiensis. Villages were chosen to represent similar ecological and entomological settings, they are middle sized and relatively isolated from one another.Figure 1Localization of the study sites in south-western Burkina Faso. This map was created under QGIS version 2.18 Las Palmas. link: https://changelog.qgis.org/en/qgis/version/2.18.0/Full size imageSpray Application Against Mosquito Swarms (SAMS) consisted of spraying diluted insecticide (Actellic 50: tap water with 1:20 concentration) at dusk by trained volunteer teams. They used the innovative technology of targeted swarm spraying with handheld sprayers and conventional broadcast space spray with backpack sprayers to achieve maximum effect. The spraying activities were conducted in eight of the ten villages. The target swarm spray was used in the four villages Kimidougou, Nastenga, Ramatoulaye and Syndombokuy. The broadcast space spray was applied in four other villages, Zeyama, Mogobasso, Lampa and Syndounkuy. The two remaining villages, Santidougou and Synbekuy were chosen as controls (Fig. 1). In each village, the potential swarm markers and the positive swarm sites were identified and geo-referenced using GPS. All concessions also were geo-referenced and labelled using paint.Procedure of the interventionTargeted swam spraying using handheld sprayersTargeted swarm spraying was carried out in four villages. Members of each team and volunteers from the selected villages were trained to target the swarms and apply an appropriate amount of spray each time. After the pre-intervention phase, all swarm sites scattered through the villages were repaired and swarm characteristics recorded. At 30 min before dusk (the estimated swarming time), a volunteer was placed in each compound with a sprayer. The objective of each volunteer was to destroy any swarm in the compound by applying insecticide with the handheld sprayer (Fig. 2A,B). Screening of the compound was continued for about 30 min until it was dark and no mosquitoes were visible. A single operator was able to effectively target 5 to 10 swarms per spray evening, depending on the distribution of swarms across the village. Spraying was carried out for 10 successive days throughout each village. The period of spraying approximately covered the period of pre-imaginal mosquito stages and was renewed after 45 days. The quantity of insecticide used was measured daily, in order to determine with precision the total quantity of insecticide used during targeted spraying.Figure 2Volunteer spraying swarms using handheld sprayers (A,B). Backpack spraying activities (C,D).Full size imageConventional broadcast spraying using Backpack sprayersThe broadcast spraying was also carried out in 4 villages but, unlike the targeted spraying, there was no direct targeting of swarms. At swarming time (estimated around 30 min at dusk) two volunteers with backpack sprayers ran through the entire village along paths between the compounds while spraying insecticide (Fig. 2C,D). As with the targeted spraying procedure, the broadcast spraying was carried out for 10 successive days in all 4 villages simultaneously, and spraying recommenced after 45 days. The quantity of insecticide used was measured daily, in order to determine with precision the total quantity of insecticide used during targeted spraying.Evaluation of the interventionA year prior to the intervention, baseline entomological data was collected in both villages to estimate mosquito density, human biting rate, female insemination rate, age structure of females and entomological inoculation rate29. The same parameters were evaluated immediately before and after intervention. The pre- and post-intervention evaluation of the abovementioned parameters were carried in both control and intervention villages at the same time. In both pre-intervention and post-intervention phases, two methods of mosquito collection were performed in each village, the human landing catch (HLC), indoor and outdoor in 4 houses for 4 successive nights, the pyrethroid spray catch (PSC) in the same10 houses and 10 randomly selected houses. To identify these, all houses in each village were coded and these codes were used to randomly select those to be sampled. All sampled sites were mapped using a global positioning system (GPS). Collected anopheline mosquitoes were sorted by taxonomic status, physiological status, and sex. Approximately, the ovaries of 200 females/month/village (100 females indoor and 100 females outdoor) were dissected to determine the physiological age, and parous females were subsequently subjected to ELISA assays to determine Plasmodium sporozoite rates. Data produced from indoor and outdoor mosquito collections were then used to estimate mosquito densities, their spatial distribution, produce a map identifying hotspots where the highest mosquito densities and biting occurred within the village, female age structure and quantify the intensity of malaria transmission. The impact of the spray was measured to see how it affected each of these parameters in the intervention villages compared to the controls.Statistical analysisThe resting mosquito abundance was assessed as the number of mosquitoes per house, the human biting rate assessed as the number of bites per person per night, the parity rate assessed as the percentage of parous females, and the insemination rate assessed as the percentage of the inseminated females. The list above defined the key entomological parameters to determine the dynamic of An. gambie s.l. populations and malaria transmission. The generalized estimating equation (GEE) method was used to estimate population averaged effect of intervention on various outcome measurements. As the GEE models do not require distributional assumptions but only specification of the mean and variance structure, they are more robust against misspecification of higher-order features of the data, and are useful when the main interest is in population averaged effects of an intervention or treatment. However, because they do not use a full likelihood model, they cannot be used for individual-specific inference30,31. Despite this shortcoming, their robustness to different types of correlation structures in the data (due to temporal ordering of measurements, or other hierarchical structure in data) makes them attractive for analyses of this type. GEE models were run in R version 3.6.232, using the package “geepack”33 for three datasets on insemination and parity rate, number of bites per person per night (NBPN), and density of adult male and female mosquitoes. To clean and plot the data the “tidyverse” family of R packages34 were used.Ethical considerationsThis study did not involve human patients. The full protocol of the study was submitted to the Institutional Ethics Committee of the “Institut de Recherche en Sciences de la Sante” for review and approval (A17-2016/CEIRES). In accordance with the approval, presentations of the project were given to the study site villagers and requests for their participation were made. During these visits the objectives, protocol and expected results were explained and discussed, as well as the implications for the households willing to take part in this study. A written consent form was signed or marked with fingerprint by the head of the households before any activity could take place in his compound. Insecticides used in this study are approved for use by the Burkina Faso insecticide regulation authority. More

Canadell, J. G. et al. Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proc. Natl. Acad. Sci. 104, 18866–18870 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Beer, C. et al. Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate. Science 329, 834–838 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Park, T. et al. Changes in timing of seasonal peak photosynthetic activity in northern ecosystems. Global. Change. Biol. 25, 2382–2395 (2019).ADS 

Google Scholar 
Wang, T. et al. Emerging negative impact of warming on summer carbon uptake in northern ecosystems. Nat. Commun. 9, 5391 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Farquhar, G. D., Von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).CAS 
PubMed 

Google Scholar 
Chen, J. M. et al. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity. Global. Biogeochem. Cy 26, GB1019 (2012).ADS 

Google Scholar 
De Pury, D. G. G. & Farquhar, G. D. Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models. Plant Cell Environ. 20, 537–557 (1997).
Google Scholar 
Zhang, Y. et al. Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data. Agr. Forest. Meteorol. 223, 116–131 (2016).ADS 

Google Scholar 
Monteith, J. L. Climate and the efficiency of crop production in Britain. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 281, 277–294 (1977).ADS 

Google Scholar 
Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience. 54, 547–560 (2004).
Google Scholar 
Yuan, W. et al. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sens. Environ. 114, 1416–1431 (2010).ADS 

Google Scholar 
Ruimy, A., Dedieu, G. & Saugier, B. TURC: A diagnostic model of continental gross primary productivity and net primary productivity. Global. Biogeochem. Cy 10, 269–285 (1996).ADS 
CAS 

Google Scholar 
Jung, M. et al. The FLUXCOM ensemble of global land-atmosphere energy fluxes. Sci. Data 6, 190076 (2019).
Google Scholar 
Bodesheim, P., Jung, M., Gans, F., Mahecha, M. D. & Reichstein, M. Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product. Earth Syst. Sci. Data 10, 1327–1365 (2018).ADS 

Google Scholar 
Joiner, J. et al. Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data. Remote Sens. 10, 1346 (2018).ADS 

Google Scholar 
Xiao, J. et al. Data-driven diagnostics of terrestrial carbon dynamics over North America. Agr. Forest. Meteorol. 197, 142–157 (2014).ADS 

Google Scholar 
Ichii, K. et al. New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression. J. Geophys. Res. Biogeosci. 122, 767–795 (2017).CAS 

Google Scholar 
Cai, W. et al. Improved estimations of gross primary production using satellite-derived photosynthetically active radiation. J. Geophys. Res. Biogeosci. 119, 110–123 (2014).
Google Scholar 
Ma, J., Yan, X., Dong, W. & Chou, J. Gross primary production of global forest ecosystems has been overestimated. Sci. Rep. 5, 10820 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cai, W. et al. Large Differences in Terrestrial Vegetation Production Derived from Satellite-Based Light Use Efficiency Models. Remote Sens. 6, 8945–8965 (2014).ADS 

Google Scholar 
Jung, M. et al. Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models. Global. Biogeochem. Cy 21, GB4021 (2007).ADS 

Google Scholar 
Yuan, W. et al. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database. Agr. Forest. Meteorol. 192-193, 108–120 (2014).ADS 

Google Scholar 
Frankenberg, C. et al. New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett. 38, L17706 (2011).ADS 

Google Scholar 
Joiner, J. et al. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos Meas Tech 6, 2803–2823 (2013).
Google Scholar 
Frankenberg, C. et al. Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2. Remote Sens. Environ. 147, 1–12 (2014).ADS 

Google Scholar 
Joiner, J. et al. Filling-in of near-infrared solar lines by terrestrial fluorescence and other geophysical effects: simulations and space-based observations from SCIAMACHY and GOSAT. Atmos Meas Tech 5, 809–829 (2012).CAS 

Google Scholar 
Köhler, P. et al. Global Retrievals of Solar‐Induced Chlorophyll Fluorescence With TROPOMI: First Results and Intersensor Comparison to OCO‐2. Geophys. Res. Lett. 45, 10,456–410,463 (2018).
Google Scholar 
Joiner, J. et al. First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences 8, 637–651 (2011).ADS 
CAS 

Google Scholar 
Guanter, L. et al. Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sens. Environ. 121, 236–251 (2012).ADS 

Google Scholar 
Du, S. et al. Retrieval of global terrestrial solar-induced chlorophyll fluorescence from TanSat satellite. Sci. Bull. 63, 1502–1512 (2018).
Google Scholar 
Baker, N. R. Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annu. Rev. Plant Biol. 59, 89–113 (2008).CAS 
PubMed 

Google Scholar 
Drusch, M. et al. The FLuorescence EXplorer Mission Concept—ESA’s Earth Explorer 8. Ieee. T. Geosci. Remote 55, 1273–1284 (2017).ADS 

Google Scholar 
Guanter, L. et al. The TROPOSIF global sun-induced fluorescence dataset from the Sentinel-5P TROPOMI mission. Earth Syst. Sci. Data, 13, 5423–5440 (2021).Roesch, A. Use of Moderate-Resolution Imaging Spectroradiometer bidirectional reflectance distribution function products to enhance simulated surface albedos. J. Geophys. Res. 109 (2004).Wan, Z. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens. Environ. 140, 36–45 (2014).ADS 

Google Scholar 
Sulla-Menashe, D., Gray, J. M., Abercrombie, S. P. & Friedl, M. A. Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product. Remote Sens. Environ. 222, 183–194 (2019).ADS 

Google Scholar 
Su, W., Charlock, T. P., Rose, F. G. & Rutan, D. Photosynthetically active radiation from Clouds and the Earth’s Radiant Energy System (CERES) products. J. Geophys. Res. 112 (2007).Still, C. J., Berry, J. A., Collatz, G. J. & Defries, R. S. Global distribution of C3and C4vegetation: Carbon cycle implications. Global. Biogeochem. Cy 17, 6-1-6-14 (2003).Zhang, Y. et al. Spatio‐temporal convergence of maximum daily light‐use efficiency based on radiation absorption by canopy chlorophyll. Geophys. Res. Lett. 45, 3508–3519 (2018).ADS 

Google Scholar 
Zhang, Z. et al. The potential of satellite FPAR product for GPP estimation: An indirect evaluation using solar-induced chlorophyll fluorescence. Remote Sens. Environ. 240, 111686 (2020).ADS 

Google Scholar 
Baker, N. R. Chlorophyll Fluorescence: A Probe of Photosynthesis In Vivo. Annu. Rev. Plant. Biol. 59, 89–113 (2008).CAS 
PubMed 

Google Scholar 
Du, S., Liu, L., Liu, X. & Hu, J. Response of canopy solar-induced chlorophyll fluorescence to the absorbed photosynthetically active radiation absorbed by chlorophyll. Remote Sens. 9, 911 (2017).ADS 

Google Scholar 
Rossini, M. et al. Analysis of Red and Far-Red Sun-Induced Chlorophyll Fluorescence and Their Ratio in Different Canopies Based on Observed and Modeled Data. Remote Sens. 8, 412 (2016).ADS 

Google Scholar 
Verrelst, J. et al. Global sensitivity analysis of the SCOPE model: What drives simulated canopy-leaving sun-induced fluorescence? Remote Sens. Environ. 166, 8–21 (2015).ADS 

Google Scholar 
Zhang, Q. et al. Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model. Remote Sens. Environ. 99, 357–371 (2005).ADS 

Google Scholar 
Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S. & Gentine, P. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).ADS 
CAS 

Google Scholar 
Li, X. & Xiao, J. A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data. Remote Sens. 11, 517 (2019).ADS 

Google Scholar 
Yu, L., Wen, J., Chang, C. Y., Frankenberg, C. & Sun, Y. High‐Resolution Global Contiguous SIF of OCO‐2. Geophys. Res. Lett. 46, 1449–1458 (2019).ADS 

Google Scholar 
Ma, Y., Liu, L., Chen, R., Du, S. & Liu, X. Generation of a Global Spatially Continuous TanSat Solar-Induced Chlorophyll Fluorescence Product by Considering the Impact of the Solar Radiation Intensity. Remote Sens. 12, 2167 (2020).ADS 

Google Scholar 
Gentine, P. & Alemohammad, S. H. Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence. Geophys. Res. Lett. 45, 3136–3146 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wen, J. et al. A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF). Remote Sens. Environ. 239, 111644 (2020).ADS 

Google Scholar 
Yang, X. et al. Solar‐induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophys. Res. Lett. 42, 2977–2987 (2015).ADS 
CAS 

Google Scholar 
Hain, C. R., Crow, W. T., Mecikalski, J. R., Anderson, M. C. & Holmes, T. An intercomparison of available soil moisture estimates from thermal infrared and passive microwave remote sensing and land surface modeling. J. Geophys. Res. 116, D15107 (2011).ADS 

Google Scholar 
Anderson, M. C., Norman, J. M., Mecikalski, J. R., Otkin, J. A. & Kustas, W. P. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. J. Geophys. Res. 112, D11112 (2007).ADS 

Google Scholar 
Scherrer, D., Bader, M. K.-F. & Körner, C. Drought-sensitivity ranking of deciduous tree species based on thermal imaging of forest canopies. Agr. Forest. Meteorol. 151, 1632–1640 (2011).ADS 

Google Scholar 
Duveiller, G. et al. A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity. Earth Syst. Sci. Data 12, 1101–1116 (2020).ADS 

Google Scholar 
Zhang, Y. et al. A global moderate resolution dataset of gross primary production of vegetation for 2000–2016. Sci. Data 4, 170165 (2017).PubMed 
PubMed Central 

Google Scholar 
Chen, T. & Guestrin, C. in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785-794 (Association for Computing Machinery).Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE 12, e0169748 (2017).PubMed 
PubMed Central 

Google Scholar 
Li, Y., Li, M., Li, C. & Liu, Z. Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Sci. Rep. 10, 9952 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tan, W., Wei, C., Lu, Y. & Xue, D. Reconstruction of All-Weather Daytime and Nighttime MODIS Aqua-Terra Land Surface Temperature Products Using an XGBoost Approach. Remote Sens. 13, 4723 (2021).ADS 

Google Scholar 
Adnan, M., Alarood, A. A. S., Uddin, M. I. & Ur Rehman, I. Utilizing grid search cross-validation with adaptive boosting for augmenting performance of machine learning models. PeerJ Comput. Sci. 8, e803 (2022).PubMed 
PubMed Central 

Google Scholar 
Chen, X. A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms. figshare https://doi.org/10.6084/m9.figshare.19336346.v2 (2022).Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl. Acad. Sci. 111, E1327–E1333 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pierrat, Z. et al. Diurnal and seasonal dynamics of solar‐induced chlorophyll fluorescence, vegetation indices, and gross primary productivity in the boreal forest. J. Geophys. Res. Biogeosci., e2021JG006588 (2022).Magney, T. S. et al. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proc. Natl. Acad. Sci. 116, 11640–11645 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Grossmann, K. et al. PhotoSpec: A new instrument to measure spatially distributed red and far-red Solar-Induced Chlorophyll Fluorescence. Remote Sens. Environ. 216, 311–327 (2018).ADS 

Google Scholar 
Li, Z. et al. Solar-induced chlorophyll fluorescence and its link to canopy photosynthesis in maize from continuous ground measurements. Remote Sens. Environ. 236, 111420 (2020).ADS 

Google Scholar 
Magney, T. S. et al. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proc. Natl. Acad. Sci. 201900278 (2019).Wei, X., Wang, X., Wei, W. & Wan, W. Use of Sun-Induced Chlorophyll Fluorescence Obtained by OCO-2 and GOME-2 for GPP Estimates of the Heihe River Basin, China. Remote Sens. 10, 2039 (2018).ADS 

Google Scholar 
Walther, S. et al. Satellite chlorophyll fluorescence measurements reveal large‐scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests. Global. Change. Biol. 22, 2979–2996 (2016).ADS 

Google Scholar 
Köhler, P., Guanter, L. & Joiner, J. A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data. Atmos. Meas. Tech. 8, 2589–2608 (2015).
Google Scholar 
Parazoo, N. C. et al. Towards a Harmonized Long‐Term Spaceborne Record of Far‐Red Solar‐Induced Fluorescence. J. Geophys. Res. Biogeosci. 124, 2518–2539 (2019).
Google Scholar 
Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7 (2020).Reichstein, M. et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global. Change. Biol. 11, 1424–1439 (2005).ADS 

Google Scholar 
Lasslop, G. et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Global. Change. Biol. 16, 187–208 (2010).ADS 

Google Scholar 
Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).PubMed 
PubMed Central 

Google Scholar 
Tong, X. et al. Increased vegetation growth and carbon stock in China karst via ecological engineering. Nat. Sustain. 1, 44–50 (2018).
Google Scholar 
Miettinen, J., Shi, C. & Liew, S. C. Deforestation rates in insular Southeast Asia between 2000 and 2010. Global. Change. Biol. 17, 2261–2270 (2011).ADS 

Google Scholar 
De, S. V. et al. Land use patterns and related carbon losses following deforestation in South America. Environ. Res. Lett. 10, 124004 (2015).ADS 

Google Scholar 
Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).ADS 

Google Scholar 
Still, C. J., Berry, J. A., Collatz, G. J. & Defries, R. S. ISLSCP II C4 Vegetation Percentage, ORNL Distributed Active Archive Center, https://doi.org/10.3334/ORNLDAAC/932 (2009).Pierrat, Z. & Stutz, J. Tower-based solar-induced fluorescence and vegetation index data for Southern Old Black Spruce forest, Zenodo, https://doi.org/10.5281/ZENODO.5884643 (2022).Magney, T. et al. Canopy and needle scale fluorescence data from Niwot Ridge, Colorado 2017-2018, CaltechDATA, https://doi.org/10.22002/D1.1231 (2019).Wan, Z., Hook, S. & Hulley, G. MOD11C1 MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 0.05Deg CMG V006, NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD11C1.006 (2015).Friedl, M. & Sulla-Menashe, D. MCD12C1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V006, NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MCD12C1.006 (2015).Schaaf, C. & Wang, Z. MCD43C4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 0.05Deg CMG V006, NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MCD43C4.006 (2015).Doelling, D. CERES Level 3 SYN1DEG-DAYTerra+Aqua HDF4 file – Edition 4A, NASA Langley Atmospheric Science Data Center DAAC, https://doi.org/10.5067/TERRA+AQUA/CERES/SYN1DEGDAY_L3.004A (2017). More

Descamps, S. et al. Diverging phenological responses of Arctic seabirds to an earlier spring. Glob. Change Biol. 25, 4081–4091 (2019).ADS 
Article 

Google Scholar 
Ramp, C., Delarue, J., Palsbøll, P. J., Sears, R. & Hammond, P. S. Adapting to a warmer ocean—seasonal shift of baleen whale movements over three decades. PLoS ONE 10, e0121374 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Insley, S. J., Halliday, W. D., Mouy, X. & Diogou, N. Bowhead whales overwinter in the Amundsen Gulf and Eastern Beaufort Sea. R. Soc. Open Sci. 8, 1 (2021).Article 

Google Scholar 
Heide-Jørgensen, M. P., Laidre, K. L., Quakenbush, L. T. & Citta, J. J. The Northwest Passage opens for bowhead whales. Biol. Lett. 8, 270–273 (2012).PubMed 
Article 

Google Scholar 
Durant, J., Hjermann, D., Ottersen, G. & Stenseth, N. Climate and the match or mismatch between predator requirements and resource availability. Clim. Res. 33, 271–283 (2007).Article 

Google Scholar 
Staudinger, M. D. et al. It’s about time: A synthesis of changing phenology in the Gulf of Maine ecosystem. Fish. Oceanogr. 28, 532–566 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Miller-Rushing, A. J., Høye, T. T., Inouye, D. W. & Post, E. The effects of phenological mismatches on demography. Philos. Trans. R. Soc. B Biol. Sci. 365, 3177–3186 (2010).Article 

Google Scholar 
Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Record, N. et al. Rapid climate-driven circulation changes threaten conservation of endangered North Atlantic right whales. Oceanography 32, 1 (2019).Article 

Google Scholar 
MacLeod, C. 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 
Learmonth, J. A. et al. Potential effects of climate change on marine mammals. Oceanogr. Mar. Biol. Annu. Rev. 44, 431–464 (2006).
Google Scholar 
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Pershing, A. J. et al. Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 350, 809–812 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Gulf of Maine Research Institute. Gulf of Maine Warming Update: 2021 the Hottest Year on Record. (2022).Saba, V. S. et al. Enhanced warming of the Northwest Atlantic Ocean under climate change. J. Geophys. Res. Oceans 121, 118–132 (2016).ADS 
Article 

Google Scholar 
Friedland, K. D. et al. Trends and change points in surface and bottom thermal environments of the US Northeast Continental Shelf Ecosystem. Fish. Oceanogr. 29, 396–414 (2020).Article 

Google Scholar 
Nye, J., Link, J., Hare, J. & Overholtz, W. Changing spatial distribution of fish stocks in relation to climate and population size on the Northeast United States continental shelf. Mar. Ecol. Prog. Ser. 393, 111–129 (2009).ADS 
Article 

Google Scholar 
Kress, S. W., Shannon, P. & O’Neal, C. Recent changes in the diet and survival of Atlantic puffin chicks in the face of climate change and commercial fishing in midcoast Maine, USA. FACETS 1, 27–43 (2017).Article 

Google Scholar 
Davis, G. E. et al. Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data. Glob. Change Biol. 26, 4812–4840 (2020).ADS 
Article 

Google Scholar 
Pace, R. M., Corkeron, P. J. & Kraus, S. D. State-space mark-recapture estimates reveal a recent decline in abundance of North Atlantic right whales. Ecol. Evol. 7, 8730–8741 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Meyer-Gutbrod, E. L. & Greene, C. H. Uncertain recovery of the North Atlantic right whale in a changing ocean. Glob. Change Biol. 24, 455–464 (2018).ADS 
Article 

Google Scholar 
Sorochan, K. A. et al. North Atlantic right whale (Eubalaena glacialis) and its food: (II) interannual variations in biomass of Calanus spp. on western North Atlantic shelves. J. Plankton Res. 41, 687–708 (2019).Article 

Google Scholar 
Friedland, K. D. et al. Spring bloom dynamics and zooplankton biomass response on the US Northeast Continental Shelf. Cont. Shelf Res. 102, 47–61 (2015).ADS 
Article 

Google Scholar 
Meyer-Gutbrod, E., Greene, C., Davies, K. & Johns, D. Ocean regime shift is driving collapse of the North Atlantic right whale population. Oceanography 34, 22–31 (2021).Article 

Google Scholar 
Knowlton, A., Hamilton, P., Marx, M., Pettis, H. & Kraus, S. Monitoring North Atlantic right whale Eubalaena glacialis entanglement rates: A 30 yr retrospective. Mar. Ecol. Prog. Ser. 466, 293–302 (2012).ADS 
Article 

Google Scholar 
Davies, K. T. A. & Brillant, S. W. Mass human-caused mortality spurs federal action to protect endangered North Atlantic right whales in Canada. Mar. Policy 104, 157–162 (2019).Article 

Google Scholar 
Kraus, S. D. & Rolland, R. M. Right whales in the urban ocean. in The urban whale: North Atlantic right whales at the crossroads 1–38 (Harvard University Press, 2010). https://doi.org/10.2307/j.ctv1pnc1q9.Winn, H. E., Price, C. A. & Sorensen, P. W. The distributional biology of the right whale (Eubalaena glacialis) in the western North Atlantic. Rep. Int. Whal. Comm. Spec. 10, 129–138 (1986).
Google Scholar 
Mayo, C. A. & Marx, M. K. Surface foraging behaviour of the North Atlantic right whale, Eubalaena glacialis, and associated zooplankton characteristics. Can. J. Zool. 68, 2214–2220 (1990).Article 

Google Scholar 
Mayo, C. A. et al. Distribution, demography, and behavior of North Atlantic right whales (Eubalaena glacialis) in Cape Cod Bay, Massachusetts, 1998–2013. Mar. Mammal Sci. 34, 979–996 (2018).Article 

Google Scholar 
Pendleton, D. E. et al. Regional-scale mean copepod concentration indicates relative abundance of North Atlantic right whales. Mar. Ecol. Prog. Ser. 378, 211–225 (2009).ADS 
Article 

Google Scholar 
Kenney, R. D., Winn, H. E. & Macaulay, M. C. Cetaceans in the Great South Channel, 1979–1989: right whale (Eubalaena glacialis). Cont. Shelf Res. 15, 385–414 (1995).ADS 
Article 

Google Scholar 
Brown, M. W. et al. Recovery strategy for the North Atlantic right whale (Eubalaena glacialis) in Atlantic Canadian waters. in Species at risk act recovery strategy series (Fisheries and Oceans Canada, 2009).Weinrich, M. T., Kenney, R. D. & Hamilton, P. K. Right whales (Eubalaena glacialis) on Jeffreys Ledge: a habitat of unrecognized importance?. Mar. Mammal Sci. 16, 326–337 (2000).Article 

Google Scholar 
Cole, T. et al. Evidence of a North Atlantic right whale Eubalaena glacialis mating ground. Endanger. Species Res. 21, 55–64 (2013).Article 

Google Scholar 
Ganley, L., Brault, S. & Mayo, C. What we see is not what there is: estimating North Atlantic right whale Eubalaena glacialis local abundance. Endanger. Species Res. 38, 101–113 (2019).Article 

Google Scholar 
Simard, Y., Roy, N., Giard, S. & Aulanier, F. North Atlantic right whale shift to the Gulf of St. Lawrence in 2015, revealed by long-term passive acoustics. Endanger. Species Res. 40, 271–284 (2019).Article 

Google Scholar 
Leiter, S. et al. North Atlantic right whale Eubalaena glacialis occurrence in offshore wind energy areas near Massachusetts and Rhode Island, USA. Endanger. Species Res. 34, 45–59 (2017).Article 

Google Scholar 
Stone, K. M. et al. Distribution and abundance of cetaceans in a wind energy development area offshore of Massachusetts and Rhode Island. J. Coast. Conserv. 21, 527–543 (2017).Article 

Google Scholar 
Vanderlaan, A., Taggart, C., Serdynska, A., Kenney, R. & Brown, M. Reducing the risk of lethal encounters: Vessels and right whales in the Bay of Fundy and on the Scotian Shelf. Endanger. Species Res. 4, 283–297 (2008).Article 

Google Scholar 
National Marine Fisheries Service. Endangered and threatened species; critical habitat for endangered North Atlantic right whale. Fed. Regist. 80, 9314–9345 (2015).
Google Scholar 
National Marine Fisheries Service. Taking of marine mammals incidental to commercial fishing operations; Atlantic large whale take reduction plan regulations; Atlantic coastal fisheries cooperative management act provisions; American lobster fishery. Fed. Regist. 85, 86878–86900 (2020).
Google Scholar 
Reeves, R. R., Breiwick, J. M. & Mitchell, E. D. History of whaling and estimated kill of right whales, Balaena glacialis, in the Northeastern United States, 1620–1924. Mar. Fish. Rev. 36, 1 (1999).
Google Scholar 
Allen, G. M. The whalebone whales of New England. Mem. Boston Soc. Nat. Hist. 8, 107–322 (1915).ADS 

Google Scholar 
CETAP (Cetacean and Turtle Assessment Program). A characterization of marine mammals and turtles in the mid- and North- Atlantic areas of the U.S. Outer Continental Shelf, final report. (1982).Kenney, R. D. & Vigness-Raposa, K. J. Marine mammals and sea turtles of Narragansett Bay, Block Island Sound, Rhode Island Sound, and nearby waters: An analysis of existing data for the Rhode Island Ocean Special Area Management Plan. in Rhode Island Ocean Special Area Management Plan; Volume 2 Appendix A: Technical Reports for the Rhode Island Ocean Special Area Management Plan. 701–1037 (Rhode Island Coastal Resources Management Council, Wakefield, RI, 2010).Pendleton, D. et al. Weekly predictions of North Atlantic right whale Eubalaena glacialis habitat reveal influence of prey abundance and seasonality of habitat preferences. Endanger. Species Res. 18, 147–161 (2012).MathSciNet 
Article 

Google Scholar 
Kraus, S. D., Kenney, R. D. & Thomas, L. A framework for studying the effects of offshore wind development on marine mammals and turtles. (2019). Report prepared for the Massachusetts Clean Energy Center, Boston, MA, and the Bureau of Ocean Energy Management, Office of Renewable Energy Programs, Sterling, VA. Anderson Cabot Center for Ocean Life, New England Aquarium, Boston, MA. 48 pp.Quintana-Rizzo, E. et al. Residency, demographics, and movement patterns of North Atlantic right whales Eubalaena glacialis in an offshore wind energy development area in southern New England, USA. Endanger. Species Res. 45, 251–268 (2021).Article 

Google Scholar 
Taylor, J. K. D., Kenney, R. D., LeRoi, D. J. & Kraus, S. D. Automated vertical photography for detecting pelagic species in multitaxon aerial surveys. Mar. Technol. Soc. J. 48, 36–48 (2014).Article 

Google Scholar 
Hamilton, P. K., Knowlton, A. R. & Marx, M. K. Right whales tell their own stories: the photo-identification catalog. in The urban whale: North Atlantic right whales at the crossroads 75–104 (Harvard University Press, 2010).Buckland, S. T., Anderson, D. R., Burnham, K. P. & Laake, J. L. Distance sampling: Estimating abundance of biological populations Vol. 50 (Chapman and Hall, 1993).MATH 
Book 

Google Scholar 
R: The R Project for Statistical Computing. https://www.r-project.org/.Miller, D. L., Rexstad, E., Thomas, L., Marshall, L. & Laake, J. L. Distance Sampling in R. J. Stat. Softw. 89, 1–28 (2019).Article 

Google Scholar 
Eberhardt, L. L., Chapman, D. G. & Gilbert, J. R. A review of marine mammal census methods. Wildl. Monogr. 1, 3–46 (1979).
Google Scholar 
Durant, S. M. et al. Long-term trends in carnivore abundance using distance sampling in Serengeti National Park, Tanzania: Serengeti carnivore trends. J. Appl. Ecol. 48, 1490–1500 (2011).Article 

Google Scholar 
Reeves, R. R. & Mitchell, E. The Long Island, New York, right whale fishery: 1650–1924. Rep. Int. Whal. Comm. 10, 201–220 (1986).
Google Scholar 
Davis, G. E. et al. Long-term passive acoustic recordings track the changing distribution of North Atlantic right whales (Eubalaena glacialis) from 2004 to 2014. Sci. Rep. 7, 13460 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jackson, J. et al. Have whales returned to a historical hotspot of industrial whaling? The pattern of southern right whale Eubalaena australis recovery at South Georgia. Endanger. Species Res. 43, 323–339 (2020).Article 

Google Scholar 
Carroll, E. L. et al. Reestablishment of former wintering grounds by New Zealand southern right whales. Mar. Mammal Sci. 30, 206–220 (2014).Article 

Google Scholar 
Charlton, C. et al. Southern right whales (Eubalaena australis) return to a former wintering calving ground: Fowlers Bay, South Australia. Mar. Mammal Sci. 35, 1438–1462 (2019).Article 

Google Scholar 
Garrigue, C. et al. Searching for humpback whales in a historical whaling hotspot of the Coral Sea, South Pacific. Endanger. Species Res. 42, 67–82 (2020).Article 

Google Scholar 
Clapham, P. J., Aguilar, A. & Hatch, L. T. Determining spatial and temporal scales for management: lessons from whaling. Mar. Mammal Sci. 24, 183–201 (2008).Article 

Google Scholar 
Watkins, W. A. & Schevill, W. E. Right whale feeding and baleen rattle. J. Mammal. 57, 58–66 (1976).Article 

Google Scholar 
Beardsley, R. C. et al. Spatial variability in zooplankton abundance near feeding right whales in the Great South Channel.. Deep Sea Res Part II Top. Stud. Oceanogr. 43, 1601–1625 (1996).ADS 
Article 

Google Scholar 
Wishner, K. F. et al. Copepod patches and right whales in the Great South Channel off New England. Bull. Mar. Sci. 43, 825–844 (1988).ADS 

Google Scholar 
Baumgartner, M., Cole, T., Clapham, P. & Mate, B. North Atlantic right whale habitat in the lower Bay of Fundy and on the SW Scotian Shelf during 1999–2001. Mar. Ecol. Prog. Ser. 264, 137–154 (2003).ADS 
Article 

Google Scholar 
Moore, M. J. & van der Hoop, J. M. The painful side of trap and fixed net fisheries: Chronic entanglement of large whales. J. Mar. Biol. 2012, 1–4 (2012).Article 

Google Scholar  More