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Growth in harvests of crops meant for exports, processing and industrial use, together with their higher yields and faster yield gains, stands out globally; at a more granular level, this was driven by specific global regions that are getting increasingly specialized in harvesting crops for these usages.Changes in global-level harvested areasAt the global scale, we find that crops harvested for direct food utilization have the highest area and have been relatively stable over the study period (Fig. 1a). However, as the total harvested hectares have increased globally (Supplementary Table 1), this has translated into decreasing fractions of crops harvested for direct food utilization, from ~51% in the 1960s (average over 1964 to 1968) to ~37% in the 2010s (average over 2009 to 2013), with a similar reduction in feed crop harvests (Table 1). Conversely, there has been a substantial increase in crops for processing, exports and industrial use (Fig. 1a, Table 1 and Supplementary Table 1). The increase in industrial crop harvests occurred after year 2000. Around the same time, harvested hectares for exported crops ramped up and by the 2010s had surpassed those of crops harvested for feed use (Fig. 1a). Crops harvested for seed usage and losses are relatively minor, and we will not discuss them further. If the global trends observed in the past 20 years continue (Fig. 1a), by 2030, crops harvested for exports, processing and industrial use will account for ~ 23%, 17% and 8% of overall harvested hectares, whereas those for food will decrease to ~29% (Table 1).Fig. 1: Sector-based global crop-utilization trends.a–d, Observed total harvested ha (a), average yield in kcal ha−1 per year (b), average yield in protein ha−1 per year (c) and average yield in fat ha−1 per year (d) in the seven sectors of food, feed, processing, export, other uses (non-food/industrial), seed and losses from 1964 to 2013, annually, and projections to 2030 based on the past 20 years. The shading shows the 90% confidence interval for the significant linear model projections.Full size imageTable 1 Sector-based global crop-utilization changesFull size tableChanges in global-level crop yieldsWe find that crops harvested for direct food usage generally have had lower yields than all other sectors at the global scale over the time period of the study (Fig. 1b–d). This is not a new phenomenon, as crops harvested for direct food utilization have always had lower yields relative to other sectors (Supplementary Table 1). What has changed, however, is the ramping up (steeper positive slopes) of industrial, export and processing crop yields (Fig. 1b–d and Table 1). At these rates, caloric yields of industrial-use crops could increase by 28% from the 2010s to 2030 compared with 24% and 21% yield increases of crops harvested for directly consumed food and for feed use (Fig. 1b). Given that caloric yields of industrial-use crops are already substantially higher than food and feed crops (2× and 1.4×, respectively, in the 2010s), the faster caloric yield increases for industrial-use crops will widen this gap (2.1× and 1.5×, respectively). Yield measurements in other units of protein and fat show similar results (Table 1, Fig. 1c,d and Supplementary Table 1).Changes in the spatial patterns of harvested areas and productionWithin country-level information on harvested areas and productivity based on utilization categories is required for developing more locally effective agricultural policies. Over the course of the study time period 1964 to 2013 (Fig. 2a,b and Supplementary Video 1), we find changes in all continents when spatially analysed at the grid-cell level, except for most parts of Africa. Even in Africa, there are locations with fractional reductions in food crop harvests over the study period, such as parts of Angola, Ghana, Nigeria and South Africa. Within these and other countries, the exact location, magnitude and direction of the change varies from one region to the next (that is, compare Fig. 2a with Fig. 2b).Fig. 2: Sector-based spatial changes in crop harvests.a,b, The fraction of a grid cell in one of seven categories—food, feed, processing, export, other (non-food/industrial use), seed and losses—in each period, 1964–1968 (a) and 2009–2013 (b).Full size imageCrops harvested for direct food utilization have been prevalent in Asia, though much has changed since the 1960s (Fig. 2a,b and Supplementary Video 1). In China, there appears to be an imaginary belt, north and west of which harvests of crops used as directly consumed food decreased between the 1960s (Fig. 2a) and 2010s (Fig. 2b), while those for other uses increased. This belt appears to roughly extend from the northern half of Jiangsu (a province on the Yellow Sea in the east), curving westwards and southwards through northern Anhui, southern Henan, central Hubei and the northern tip of Hunan, and then turning sharply south and splitting Guangdong (a province on the South China Sea) through the middle. The sector gaining from the 10–20% fractional food harvest reduction varies. The increase in crops for feed, processing and industrial usage increases as one moves northward, especially north of Jiangsu and Anhui (Fig. 2a,b and Supplementary Video 1).Similarly, in India, there is a north–south zone encompassing eastern Haryana in the north, moving southwards through eastern Rajasthan, western Madhya Pradesh to eastern Maharashtra in the south, where there was a drastic reduction in crops harvested for direct food utilization over the study period (Fig. 2 and Supplementary Video 1); crops harvested for processing primarily increased. Changes in South and Southeast Asia over the study period are primarily away from once-dominant harvests of directly consumed food crops to feed crops, followed by processing crops, export crops and industrial-use crops, as in Myanmar and Thailand. In Malaysia, the growth was in export and industrial-usage crops, whereas in Indonesia, it was export crops and smaller increases in industrial-utilization crops. Central Asian states, especially Kazakhstan and some parts of Russia, witnessed a large reduction in crops harvested for direct food use over the study period, replaced by the crops destined for exports between the two periods (Fig. 2 and Supplementary Video 1).In Australia in the 1960s, food crops were harvested everywhere, accounting for ~10% of the total, which declined to ~5% by the 2010s. This was accompanied by small reductions in crops harvested for feed and export and balanced mainly by increases in crops for processing and industrial utilization (Fig. 2 and Supplementary Video 1).In Europe in the 1960s, crops were dominantly harvested for food and feed, but by the 2010s, this changed to include crops harvested for processing (Fig. 2 and Supplementary Video 1). In France, major reductions in feed crops have been balanced by growth in processing, export and industrial-use crops. In Spain, the primary change is from crops harvested for direct food to those of feed. In Germany, crops harvested for export have replaced those for direct food utilization.Latin America used to dominantly harvest food crops (as in Mexico) or food and feed crops (as in Brazil and Argentina) (Fig. 2 and Supplementary Video 1). Midwestern Brazil used to harvest only food crops, and feed and processing crop harvests were restricted to the Atlantic states (the 1960s; Fig. 2a), but by the 2010s (Fig. 2b), harvests of food crops had become a negligible fraction in Midwestern Brazil (as in Mato Grosso), and crops harvested for processing and exports are dominant now. In the Atlantic states of Brazil, one of the major changes is the increased proportion of harvests for industrial crops. In Argentina, over the study period, the proportion of crops harvested for food and feed has decreased, and this utilization has been mainly replaced by crops harvested for processing; crops harvested for exports changed, but the direction of change was spatially heterogeneous across Argentina (Fig. 2 and Supplementary Video 1). In Mexico, the primary change is the reduction in the fraction of crops harvested for direct food consumption and the increased harvests of crops for feed.Crops harvested for food and feed are also on the decline proportionally in North America. The United States has experienced a change from the dominance of food and feed crops in the 1960s to processing and industrial-usage crops in the 2010s. Detailed changes in the United States and Canada vary from one location to the next (Fig. 2), though the major change is the lower fraction of crops harvested for direct food consumption.Results are similar when viewed through the lens of calories, protein and fat with local-level differences as yields vary based on the measurement units (Supplementary Fig. 1). Further dramatic changes can be expected if observed linear trends from 1994 to 2013 at each grid cell continued until 2030 (Supplementary Fig. 2).Calories harvested in 2030 and achieving UN SDG 2We compare the extra food calories that will potentially be harvested in 2030 (Fig. 3a and Supplementary Data 2) to those required for both the projected extra population and feeding the projected undernourished population in each country (Fig. 3b and Supplementary Data 2). As an extreme case, we also compared whether total calories (all seven utilization sectors) would be sufficient (Fig. 3c and Supplementary Data 2). Altogether, we evaluated 156 countries, of which 86 had reported undernourished populations (Supplementary Data 2). On the basis of the minimum dietary energy requirement (MDER), we find that countries with reported undernourished populations will have a shortfall of ~675.4 trillion kcal per year to nourish the increased population and the expected undernourished from their extra harvested food calories. However, compared with the more realistic average dietary energy requirement (ADER), this shortfall will be ~993.9 trillion kcal per year (or ~70% from requirements) in 2030 (15 additional scenarios of undernourished populations in 2030 (provided in Supplementary Data 3) show global calorie shortfalls may similarly range from ~587.2 trillion kcal per year to ~1,269.3 trillion kcal per year based on the MDER level of nutrition requirement, and ~880.7 trillion kcal per year to ~1,755.6 trillion kcal per year based on the more realistic ADER level of nutrition requirement in 2030).Fig. 3: Meeting UN SDG goal 2 in 2030.a, Same as Fig. 2 but for the projected kcal ha−1 per year in 2030 per utilization sector and then mapping the fraction of total kcal ha−1 per year projected as harvested. b, Shortfall or gap from kcal per year harvested in 2030 as crops for direct food use and those to plug the gap from population growth and/or undernourished population. Computed based on the 2018 to 2020 ADER number for the country. c, Same as b but the kcal per year harvested used for computation is the total across all the seven sectors and shortfall is from whether the total calories harvested were used for direct food consumption (little to no processing).Full size imageCountries reporting undernourishment can, however, meet their requirement of extra calories in 2030 for both population change and those for the undernourished if calories from other utilization sectors are diverted and consumed directly as food calories (Fig. 3c and Supplementary Data 2 and 3). Though at the global scale, it appears that countries with high levels of undernourishment in 2030 can divert just a portion of their total harvested calories and meet some of the requirements of UN’s SDG 2 (ref. 4). In reality, many of the individual countries concentrated in sub-Saharan Africa have limited scope of diversion of calories from other sectors such as feed, processing or exports as crops for direct food use, as they already harvest most crops for direct food consumption (Fig. 2 and Supplementary Figs. 1 and 2). As such, many countries in this region may see deepening reliance on food imports. Note that the UN’s second SDG goal is broader in scope, including efforts to end malnutrition and increase agricultural productivity, among other goals4. Reconfiguration planning19 can use our spatially detailed information (Figs. 2 and 3, Supplementary Figs. 1 and 2 and Supplementary Data 2 and 3) in conjunction with policies that incentivize increased food crop harvests globally and ensure their equitable distribution to undernourished regions when local production is not sufficient20,21. This will require supply chain management22,23 and detailed analysis of optimization scenarios24 with our maps and tables as an important step linking specific production regions with the initial use of that production. More

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Spillover events, in which a pathogen that originates in animals jumps into people, have probably triggered every viral pandemic that’s occurred since the start of the twentieth century1. What’s more, an August 2021 analysis of disease outbreaks over the past four centuries indicates that the yearly probability of pandemics could increase several-fold in the coming decades, largely because of human-induced environmental changes2.Fortunately, for around US$20 billion per year, the likelihood of spillover could be greatly reduced3. This is the amount needed to halve global deforestation in hotspots for emerging infectious diseases; drastically curtail and regulate trade in wildlife; and greatly improve the ability to detect and control infectious diseases in farmed animals.That is a small investment compared with the millions of lives lost and trillions of dollars spent in the COVID-19 pandemic. The cost is also one-twentieth of the statistical value of the lives lost each year to viral diseases that have spilled over from animals since 1918 (see ‘Spillovers: a growing threat’), and less than one-tenth of the economic productivity erased per year1.

Source: Ref. 1

Yet many of the international efforts to better defend the world from future outbreaks, prompted by the COVID-19 pandemic, still fail to prioritize the prevention of spillover. Take, for example, the Independent Panel for Pandemic Preparedness and Response, established by the World Health Organization (WHO). The panel was convened in September 2020, in part to ensure that any future infectious-disease outbreak does not become another pandemic. In its 86-page report released last May, wildlife is mentioned twice; deforestation once.We urge the decision-makers currently developing three landmark international endeavours to make the prevention of spillover central to each.First, the G20 group of the world’s 20 largest economies provisionally agreed last month to create a global fund for pandemics. If realized, this could provide funding at levels that infectious-disease experts have been recommending for decades — around $5 per person per year globally (see go.nature.com/3yjitwx). Second, an agreement to improve global approaches to pandemics is under discussion by the World Health Assembly (WHA), the decision-making body of the WHO. Third, a draft framework for biodiversity conservation — the post-2020 global biodiversity framework — is being negotiated by parties to the Convention on Biological Diversity.Designed in the right way, these three international endeavours could foster a more proactive global approach to infectious diseases. This opportunity — to finally address the factors that drive major disease outbreaks, many of which also contribute to climate change and biodiversity loss — might not present itself again until the world faces another pandemic.Four actions The risk of spillover is greater when there are more opportunities for animals and humans to make contact, for instance in the trade of wildlife, in animal farming or when forests are cleared for mining, farming or roads. It is also more likely to happen under conditions that increase the likelihood of infected animals shedding viruses – when they are housed in cramped conditions, say, or not fed properly.Decades of research from epidemiology, ecology and genetics suggest that an effective global strategy to reduce the risk of spillover should focus on four actions1,3.First, tropical and subtropical forests must be protected. Various studies show that changes in the way land is used, particularly tropical and subtropical forests, might be the largest driver of emerging infectious diseases of zoonotic origin globally4. Wildlife that survives forest clearance or degradation tends to include species that can live alongside people, and that often host pathogens capable of infecting humans5. For example, in Bangladesh, bats that carry Nipah virus — which can kill 40–75% of people infected — now roost in areas of high human population density because their forest habitat has been almost entirely cleared6.Furthermore, the loss of forests is driving climate change. This could in itself aid spillover by pushing animals, such as bats, out of regions that have become inhospitable and into areas where many people live7.Yet forests can be protected even while agricultural productivity is increased — as long as there is enough political will and resources8. This was demonstrated by the 70% reduction in deforestation in the Amazon during 2004–12, largely through better monitoring, law enforcement and the provision of financial incentives to farmers. (Deforestation rates began increasing in 2013 due to changes in environmental legislation, and have risen sharply since 2019 during Jair Bolsonaro’s presidency.)Second, commercial markets and trade of live wild animals that pose a public-health risk must be banned or strictly regulated, both domestically and internationally.Doing this would be consistent with the call made by the WHO and other organizations in 2021 for countries to temporarily suspend the trade in live caught wild mammals, and to close sections of markets selling such animals. Several countries have already acted along these lines. In China, the trade and consumption of most terrestrial wildlife has been banned in response to COVID-19. Similarly, Gabon has prohibited the sale of certain mammal species as food in markets.

A worker in a crowded chicken farm in Anhui province, China.Credit: Jianan Yu/Reuters

Restrictions on urban and peri-urban commercial markets and trade must not infringe on the rights and needs of Indigenous peoples and local communities, who often rely on wildlife for food security, livelihoods and cultural practices. There are already different rules for hunting depending on the community in many countries, including Brazil, Canada and the United States.Third, biosecurity must be improved when dealing with farmed animals. Among other measures, this could be achieved through better veterinary care, enhanced surveillance for animal disease, improvements to feeding and housing animals, and quarantines to limit pathogen spread.Poor health among farmed animals increases their risk of becoming infected with pathogens — and of spreading them. And nearly 80% of livestock pathogens can infect multiple host species, including wildlife and humans9.Fourth, particularly in hotspots for the emergence of infectious diseases, people’s health and economic security should be improved.People in poor health — such as those who have malnutrition or uncontrolled HIV infection — can be more susceptible to zoonotic pathogens. And, particularly in immunosuppressed individuals such as these, pathogens can mutate before being passed on to others10.What’s more, some communities — especially those in rural areas — use natural resources to produce commodities or generate income in a way that brings them into contact with wildlife or wildlife by-products. In Bangladesh, for example, date palm sap, which is consumed as a drink in various forms, is often collected in pots attached to palm trees. These can become contaminated with bodily substances from bats. A 2016 investigation linked this practice to 14 Nipah virus infections in humans that caused 8 deaths11.Providing communities with both education and tools to reduce the risk of harm is crucial. Tools can be something as simple as pot covers to prevent contamination of date palm sap, in the case of the Bangladesh example.In fact, providing educational opportunities alongside health-care services and training in alternative livelihood skills, such as organic agriculture, can help both people and the environment. For instance, the non-governmental organization Health in Harmony in Portland, Oregon, has invested in community-designed interventions in Indonesian Borneo. During 2007–17, these contributed to a 90% reduction in the number of households that were reliant on illegal logging for their main livelihood. This, in turn, reduced local rainforest loss by 70%. Infant mortality also fell by 67% in the programme’s catchment area12.Systems-oriented interventions of this type need to be better understood, and the most effective ones scaled up.Wise investmentSuch strategies to prevent spillover would reduce our dependence on containment measures, such as human disease surveillance, contact tracing, lockdowns, vaccines and therapeutics. These interventions are crucial, but are often expensive and implemented too late — in short, they are insufficient when used alone to deal with emerging infectious diseases.The COVID-19 pandemic has exposed the real-world limitations of these reactive measures — particularly in an age of disinformation and rising populism. For example, despite the US federal government spending more than $3.7 trillion on its pandemic response as of the end of March, nearly one million people in the United States — or around one in 330 — have died from COVID-19 (see go.nature.com/39jtdfh and go.nature.com/38urqvc). Globally, between 15 million and 21 million lives are estimated to have been lost during the COVID-19 pandemic beyond what would be expected under non-pandemic conditions (known as excess deaths; see Nature https://doi.org/htd6; 2022). And a 2021 model indicates that, by 2025, $157 billion will have been spent on COVID-19 vaccines alone (see go.nature.com/3jqds76).

A farmer in Myanmar gathers sap from a palm tree to make wine. Contamination of the collection pots with excretions from bats can spread diseases to humans.Credit: Wolfgang Kaehler/LightRocket via Getty

Preventing spillover also protects people, domesticated animals and wildlife in the places that can least afford harm — making it more equitable than containment. For example, almost 18 months since COVID-19 vaccines first became publicly available, only 21% of the total population of Africa has received at least one dose. In the United States and Canada, the figure is nearly 80% (see go.nature.com/3vrdpfo). Meanwhile, Pfizer’s total drug sales rose from $43 billion in 2020 to $72 billion in 2021, largely because of the company’s COVID-19 vaccine, the best-selling drug of 202113.Lastly, unlike containment measures, actions to prevent spillover also help to stop spillback, in which zoonotic pathogens move back from humans to animals and then jump again into people. Selection pressures can differ across species, making such jumps a potential source of new variants that can evade existing immunity. Some researchers have suggested that spillback was possibly responsible for the emergence of the Omicron variant of SARS-CoV-2 (see Nature 602, 26–28; 2022).Seize the dayOver the past year, the administration of US President Joe Biden and two international panels (one established in 2020 by the WHO and the other in 2021 by the G20) have released guidance on how to improve approaches to pandemics. All recommendations released so far acknowledge spillover as the predominant cause of emerging infectious diseases. None adequately discusses how that risk might be mitigated. Likewise, a PubMed search for the spike protein of SARS-CoV-2 yields thousands of papers, yet only a handful of studies investigate coronavirus dynamics in bats, from which SARS-CoV-2 is likely to have originated14.Spillover prevention is probably being overlooked for several reasons. Upstream animal and environmental sources of pathogens might be being neglected by biomedical researchers and their funders because they are part of complex systems — research into which does not tend to lead to tangible, profitable outputs. Also, most people working in public health and biomedical sciences have limited training in ecology, wildlife biology, conservation and anthropology.There is growing recognition of the importance of cross-sectoral collaboration, including soaring advocacy for the ‘One Health’ approach — an integrated view of health that recognizes links between the environment, animals and humans. But, in general, this has yet to translate into action to prevent pandemics.Another challenge is that it can take decades to realize the benefits of preventing spillover, instead of weeks or months for containment measures. Benefits can be harder to quantify for spillover prevention, no matter how much time passes, because, if measures are successful, no outbreak occurs. Prevention also runs counter to individual, societal and political tendencies to wait for a catastrophe before taking action.The global pandemic fund, the WHA pandemic agreement and the post-2020 global biodiversity framework all present fresh chances to shift this mindset and put in place a coordinated global effort to reduce the risk of spillover alongside crucial pandemic preparedness efforts.Global fund for pandemicsFirst and foremost, a global fund for pandemics will be key to ensuring that the wealth of evidence on spillover prevention is translated into action. Funding for spillover prevention should not be folded into existing conservation funds, nor draw on any other existing funding streams.Investments must be targeted to those regions and practices where the risk of spillover is greatest, from southeast Asia and Central Africa to the Amazon Basin and beyond. Actions to prevent spillover in these areas, particularly by reducing deforestation, would also help to mitigate climate change and reduce loss of biodiversity. But conservation is itself drastically underfunded. As an example, natural solutions (such as conservation, restoration and improved management of forests, wetlands and grasslands) represent more than one-third of the climate mitigation needed by 2030 to stabilize warming to well below 2 °C15. Yet these approaches receive less than 2% of global funds for climate mitigation16. (Energy systems receive more than half.)In short, the decision-makers backing the global fund for pandemics must not assume that existing funds are dealing with the threat of spillover — they are not. The loss of primary tropical forest was 12% higher in 2020 than in 2019, despite the economic downturn triggered by COVID-19. This underscores the continuing threat to forests.Funding must be sustained for decades to ensure that efforts to reduce the risk of spillover are in place long enough to yield results.WHA pandemic agreementIn 2020, the president of the European Council, Charles Michel, called for a treaty to enable a more coordinated global response to major epidemics and pandemics. Last year, more than 20 world leaders began echoing this call, and the WHA launched the negotiation of an agreement (potentially, a treaty or other international instrument) to “strengthen pandemic prevention, preparedness, and response” at the end of 2021.Such a multilateral agreement could help to ensure more-equitable international action around the transfer of scientific knowledge, medical supplies, vaccines and therapeutics. It could also address some of the constraints currently imposed on the WHO, and define more clearly the conditions under which governments must notify others of a potential disease threat. The COVID-19 pandemic exposed the shortcomings of the International Health Regulations on many of these fronts17. (This legal framework defines countries’ rights and obligations in the handling of public-health events and emergencies that could cross borders.)We urge negotiators to ensure that the four actions to prevent spillover outlined here are prioritized in the WHA pandemic agreement. For instance, it could require countries to create national action plans for pandemics that include reducing deforestation and closing or strictly regulating live wildlife markets. A reporting mechanism should also be developed to evaluate progress in implementing the agreement. This could build on experience from existing schemes, such as the WHO Joint External Evaluation process (used to assess countries’ capacities to handle public-health risks) and the verification regime of the Chemical Weapons Convention.Commitments to expand pathogen surveillance at interfaces between humans, domesticated animals and wildlife — from US mink farms and Asian wet markets to areas of high deforestation in South America — should also be wrapped into the WHA agreement. Surveillance will not prevent spillover, but it could enable earlier detection and better control of zoonotic outbreaks, and provide a better understanding of the conditions that cause them. Disease surveillance would improve simply through investing in clinical care for both people and animals in emerging infectious-disease hotspots.Convention on Biological DiversityWe are in the midst of the sixth mass extinction, and activities that drive the loss of biodiversity, such as deforestation, also contribute to the emergence of infectious disease. Meanwhile, epidemics and pandemics resulting from the exploitation of nature can lead to further conservation setbacks — because of economic damage from lost tourism and staff shortages affecting management of protected areas, among other factors18. Also, pathogens that infect people can be transmitted to other animals and decimate those populations. For instance, an Ebola outbreak in the Republic of Congo in 2002–03 is thought to have killed 5,000 gorillas19.Yet the global biodiversity framework currently being negotiated by the Convention on Biological Diversity fails to explicitly address the negative feedback cycle between environmental degradation, wildlife exploitation and the emergence of pathogens. The first draft made no mention of pandemics. Text about spillover prevention was proposed in March, but it has yet to be agreed on.Again, this omission stems largely from the siloing of disciplines and expertise. Just as the specialists relied on for the WHA pandemic agreement tend to be those in the health sector, those informing the Convention on Biological Diversity tend to be specialists in environmental science and conservation.The global biodiversity framework, scheduled to be agreed at the Conference of the Parties later this year, must strongly reflect the environment–health connection. This means explicitly including spillover prevention in any text relating to the exploitation of wildlife and nature’s contributions to people. Failing to connect these dots weakens the ability of the convention to achieve its own objectives around conservation and the sustainable use of resources.Preventive health careA reactive response to catastrophe need not be the norm. In many countries, preventive health care for chronic diseases is widely embraced because of its obvious health and economic benefits. For instance, dozens of colorectal cancer deaths are averted for every 1,000 people screened using colonoscopies or other methods20. A preventive approach does not detract from the importance of treating diseases when they occur.With all the stressors now being placed on the biosphere — and the negative implications this has for human health — leaders urgently need to apply this way of thinking to pandemics. More

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Cockroach strainsAll cockroaches were maintained on rodent diet (Purina 5001, PMI Nutrition International, St. Louis, MO) and distilled water at 27 °C, ~40% RH, and a 12:12 h L:D cycle. The WT colony (Orlando Normal) was collected in Florida in 1947 and has served as a standard insecticide-susceptible strain. The GA colony (T-164) was collected in 1989, also in Florida, and shown to be aversive to glucose; continued artificial selection with glucose-containing toxic bait fixed the homozygous GA trait in this population (approximately 150 generations as of 2020).Generating recombinant lines and life history dataTo homogenize the genetic backgrounds of the WT and GA strains, two recombinant colonies were initiated in 2013 by crossing 10 pairs of WT♂ × GA♀ and 10 pairs of GA♂ × WT♀ (Fig. 3a). At the F8 generation (free bulk mating without selection), 400 cockroaches were tested in two-choice feeding assays (see below) that assessed their initial response to tastants, as described in previous studies11,26. The cockroaches were separated into glucose-accepting and glucose-rejecting groups by the rapid Acceptance-Rejection assay (described in Feeding Bioassays). These colonies were bred for three more generations, and 200 cockroaches from each group were assayed in the F11 generation and backcrossed to obtain homozygous glucose-accepting (aa) and glucose-averse (AA) lines. Similar results were obtained in both directions of the cross, confirming previous findings of no sex linkage of the GA trait27. These two lines were defined as WT_aa (homozygotes, glucose-accepting) and GA_AA (homozygotes, glucose-averse). To obtain heterozygous GA cockroaches, GA_Aa, a single intercross group was generated from crosses of 10 pairs of WT_aa♂ × GA_AA♀ and 10 pairs of GA_AA♂ × WT_aa♀.The GA trait follows Mendelian inheritance. Therefore, we used backcrosses, guided by two-choice feeding assays and feeding responses in Acceptance-rejection assays, to determine the homozygosity of WT and GA cockroaches. The cross of WT♂ × WT♀ produced homozygous F1 cockroaches showing maximal glucose-acceptance. The cross of GA♂ × GA♀ produced homozygous F1 cockroaches showing maximal glucose-aversion. The cross of WT × GA produced F1 heterozygotes with intermediate glucose-aversion. When the F1 heterozygotes were backcrossed with WT cockroaches, they produced F2 cockroaches with a 1:1 ratio of WT and GA phenotypes.The two-choice feeding assay assessed whether cockroaches accepted or rejected glucose (binary: yes-no). Insects were held for 24 h without water, or starved without food and water. Either 10 adults or 2 day-old first instar siblings (30–40) were placed in a Petri dish (either 90 mm or 60 mm diameter × 15 mm height). Each Petri dish contained two agar discs: one disc contained 1% agar and 1 mmol l−1 red food dye (Allura Red AC), and the second disc contained 1% agar, 0.5 mmol l−1 blue food dye (Erioglaucine disodium salt) and either 1000 mmol l−1 or 3000 mmol l−1 glucose. The assay duration was 2 h during the dark phase of the insects’ L:D cycle. After each assay, the color of the abdomen of each cockroach was visually inspected under a microscope to infer the genotype.We assessed whether the recombinant colonies had different traits from the parental WT and GA lines. We paired single newly eclosed females (day 0) with single 10–12 days-old males of the same line in a Petri dish (90 mm diameter, 15 mm height) with fresh distilled water in a 1.5 ml microcentrifuge tube and a pellet of rodent food, and monitored when they mated. When females formed egg cases, each gravid female was placed individually in a container (95 × 95 × 80 mm) with food and water until the eggs hatched. After removing the female, her offspring were monitored until adult emergence. We recorded the time to egg hatch, first appearance of each nymphal stage, first appearance of adults and the end of adult emergence. The first instar nymphs and adults in each cohort were counted to obtain measures of survivorship. Although there were significant differences in some of these parameters across all four strains, we found no significant differences between the two recombinant lines, except mating success, which was significantly lower in GA_AA♀ than WT_aa♀ (Supplementary Table 11).Mating bioassaysAll mating sequences were recorded using an infra-red-sensitive camera (Polestar II EQ610, Everfocus Electronics, New Taipei City, Taiwan) coupled to a data acquisition board and analyzed by searchable and frame-by-frame capable software (NV3000, AverMedia Information) at 27 °C, ~40% RH and a 12:12 h L:D cycle. For behavioral analysis, tested pairs were classified into two groups: mated (successful courtship) and not-mated (failed courtship). Four distinct behavioral events (Fig. 1c, Contact, Wing raising, Nuptial feeding, and Copulation) were analyzed using seven behavioral parameters as shown in Supplementary Table 2.We extracted behavioral data from successful courtship sequences, defined as courtship that led to Copulation. For failed courtship sequences, we extracted the behavioral data from the first courtship of both mated and not-mated groups, because most pairs in both groups failed to copulate in their first encounter, and there were no significant differences in behavioral parameters between the two groups.To assay female choice, we conducted two-choice mating assays (Fig. 1a). A single focal WT♀ or GA♀ and two males, one WT and one GA, were placed in a Petri dish (90 mm diameter, 15 mm height) with fresh distilled water in a 1.5 ml microcentrifuge tube and a pellet of rodent food (n = 25 WT♀ and 27 GA♀). To assay male choice, a single focal WT♂ or GA♂ was given a choice of two females, one WT♀ and one GA♀ (n = 27 WT♂ and 18 GA♂). Experiments were started using 0 day-old sexually unreceptive females and 10–12 days-old sexually mature males. Newly emerged (0 day-old) females were used to avoid the disruption of introducing a sexually mature female into the bioassay. B. germanica females become sexually receptive at 5–7 days of age, so the mating behavior of the focal insect was video-recorded for several days until they mated. Fertility of mated females was evaluated by the number of offspring produced. We assessed the gustatory phenotype of nymphs (either WT-type or GA-type) to determine which of the two adult cockroaches mated with the focal insect. Each gravid female was maintained individually in a container (95 × 95 × 80 mm) with food and water until the eggs hatched. Two day-old first instar nymphs were starved for one day without water and food, and then they were tested in Two-choice feeding assays using 1000 mmol l−1 glucose-containing agar with 0.5 mmol l−1 blue food dye vs. plain sugar-free agar with 1 mmol l−1 red food dye. If all the nymphs chose the glucose-containing agar, their parents were considered WT♂ and WT♀. When all the nymphs showed glucose-aversion, they were raised to the adult stage. Newly emerged adults were backcrossed with WT cockroaches, and their offspring were tested in the Two-choice assay. When the parents were both GA, 100% of the offspring exhibited glucose-aversion. When the parents were WT and GA, the offspring showed a 1:1 ratio of glucose-accepting and glucose-aversive behavior. Mate choice, mating success ratio and the number of offspring were analyzed statistically.We conducted no-choice mating assay using the WT and GA strains (Fig. 1b, d). A female and a male were placed in a Petri dish with fresh water and a piece of rodent food and video-recorded for 24 h. The females were 5–7 days-old and males were 10–12 days-old. Four treatment pairs were tested: WT♂ × WT♀ (n = 20, 18 and 14 pairs for 5, 6 and 7 day-old females, respectively); GA♂ × GA♀ (n = 23, 22 and 35 pairs); GA♂ × WT♀ (n = 21, 14 and 17 pairs); and WT♂ × GA♀ (n = 33, 19 and 15 pairs).To confirm that gustatory stimuli guide nuptial feeding, we artificially augmented the male nuptial secretion and assessed whether the duration of nuptial feeding and mating success of GA♀ were affected (Fig. 2c). Before starting the mating assay with 5 day-old GA♀, 10–12 days-old WT♂ were separated into three groups: A control group did not receive any augmentation; A water control group received distilled water with 1 mmol l−1 blue dye (+Blue); A fructose group received 3000 mmol l−1 fructose solution with blue dye (+Blue+Fru). Approximately 50 nl of the test solution was placed into the tergal gland reservoirs using a glass microcapillary. No-choice mating assays were carried out for 24 h. n = 20–25 pairs for each treatment.We evaluated the association of short nuptial feeding (Fig. 1c) and the GA trait we conducted no-choice mating assays using females from the recombinant lines (Fig. 3c). Before starting each mating assay with 4 day-old females from the WT, GA and recombinant lines (WT_aa, GA_AA and GA_Aa), the EC50 for glucose was obtained by the instantaneous Acceptance-Rejection assay using 0, 10, 30, 100, 300, 1000 and 3000 mmol l−1 glucose (WT♀ and WT_aa♀, non-starved; GA♀, GA_AA♀ and GA_Aa♀, 1-day starved). After the Acceptance-Rejection assay, GA_Aa♀ were separated into two groups according to their sensitivity for rejecting glucose; the GA_Aa_high sensitivity group rejected glucose at 100 and 300 mmol l−1, whereas the GA_Aa_low sensitivity group rejected glucose at 1000 and 3000 mmol l−1. We paired these females with 10–12 days-old WT♂ (n = 15 WT_aa♀, n = 20 GA_AA♀, n = 20 GA_Aa_high♀ and n = 17 GA_Aa_low♀).Feeding bioassayWe conducted two feeding assays: Acceptance-Rejection assay and Consumption assay. The Acceptance-Rejection assay assessed the instantaneous initial responses (binary: yes-no) of cockroaches to tastants, as previously described7,22,27. Briefly, acceptance means that the cockroach started drinking. Rejection means that the cockroach never initiated drinking. The percentage of positive responders was defined as the Number of insects accepting tastants/Total number of insects tested. The effective concentration (EC50) for each tastant was obtained from dose-response curves using this assay. The Consumption assay was previously described27. Briefly, we quantified the amount of test solution females ingested after they started drinking. Females were observed until they stopped drinking, and we considered this a single feeding bout.We used the Acceptance-Rejection assay and Consumption assay, respectively, to assess the sensitivity of 5 day-old WT♀ and GA♀ for accepting and consuming the WT♂ nuptial secretion (Fig. 2a, b). The secretion was diluted with HPLC-grade water to 0.001, 0.01, 0.03, 0.1, 0.3 and 1 male-equivalents/µl (n = 20 non-starved females each). The amount of nuptial secretion consumed was tested at 0.1 male-equivalents/µl in the Consumption assay (n = 10 each).The Acceptance-Rejection assay was used to calculate the effective concentration (EC50) of glucose for females in the WT, GA and recombinant lines (Fig. 3a, b). A glucose concentration series of 0.1, 1, 10, 100 and 1000 mmol l−1 was tested with one-day starved 4-day old females (n = 65 GA_Aa♀, n = 50 GA_AA♀ and n = 50 GA♀) and non-starved females (n = 50 WT_aa♀ and n = 16 WT♀).The effects of female saliva on feeding responses of 5 day-old WT♀ and GA♀ were tested using the Acceptance-Rejection assay (Fig. 4a). Freshly collected saliva of WT♀ and GA♀ was immediately used in experiments. Assays were prepared as follows: 3 µl of 200 mmol l−1 maltose or maltotriose were mixed with 3 µl of either HPLC-grade water or saliva of WT♀ or GA♀. The final concentration of each sugar was 100 mmol l−1 in a total volume of 6 µl. This concentration represented approximately the acceptance EC70 for WT♀ and GA♀27. Nuptial secretion (1 µl representing 10 male-equivalents) was mixed with 1 µl of either HPLC-grade water or saliva from WT♀ or GA♀, and 8 µl of HPLC-grade water was added to the mix. The final concentration of the nuptial secretion was 1 male-equivalent/µl in a total volume of 10 µl. This concentration also represented approximately the acceptance EC70 for WT♀ and GA♀ (Fig. 2a). The mix of saliva and either sugar or nuptial secretion was incubated for 300 s at 25 °C. Additionally, we tested the effect of only saliva in the Acceptance-Rejection assay. Either 1-day starved or non-starved females were tested with water only and then a 1:1 mixture of saliva and water. Saliva alone did not affect acceptance or rejection of stimuli. n = 20–33 females from each strain.To evaluate whether salivary enzymes are involved in the hydrolysis of oligosaccharides, the contribution of salivary glucosidases was tested using the glucosidase inhibitor acarbose in the Acceptance-Rejection assay (Fig. 4b), as previously described27. We first confirmed that the range of 0–125 mmol l−1 acarbose in HPLC-grade water did not disrupt the acceptance and rejection of tastants. Test solutions were prepared as follows: 2 µl of either HPLC-grade water or saliva of GA♀ was mixed with 1 µl of either 250 µmol l−1 of acarbose or HPLC-grade water, then the mixture was added to 1 µl of 400 mmol l−1 of either maltose or maltotriose solution. The total volume was 4 µl, with the final concentration of sugar being 100 mmol l−1. For assays with nuptial secretion, 1 µl of either HPLC-grade water or saliva from 5 day-old GA♀ was mixed with 0.5 µl of either 250 µmol l−1 of acarbose or HPLC-grade water. This mixture was added to 0.5 µl of 10 male-equivalents of nuptial secretion (i.e., 20 male-equivalents/µl). HPLC-grade water was added for a total volume of 10 µl and a final concentration of 1 male-equivalent/µl. The mix of saliva and either sugars or nuptial secretion was incubated for 5 min at 25 °C. All test solutions contained blue food dye. Test subjects were 5 day-old GA♀ and 20–25 females were tested in each assay.Nuptial secretion and saliva collectionsThe nuptial secretion of WT♂ was collected by the following method: Five 10–12 days-old males were placed in a container (95 × 95 × 80 mm) with 5 day-old GA♀. After the males displayed wing-raising courtship behavior toward the females, individual males were immediately decapitated and the nuptial secretion in their tergal gland reservoirs was drawn into a calibrated borosilicate glass capillary (76 × 1.5 mm) under the microscope. The nuptial secretions from 30 males were pooled in a capillary and stored at −20 °C until use. Saliva from 5 day-old WT♀ and GA♀ was collected by the following method: individual females were briefly anesthetized with carbon dioxide under the microscope and the side of the thorax was gently squeezed. A droplet of saliva that accumulated on the mouthparts was then collected into a microcapillary (10 µl, Kimble Glass). Fresh saliva was immediately used in experiments.GC-MS procedures for analysis of sugarsStandards of D-( + )-glucose (Sigma-Aldrich), D-( + )-maltose (Fisher Scientific) and maltotriose (Sigma-Aldrich) were diluted in HPLC-grade water (Fisher Scientific) at 10, 50, 100, 500 and 1000 ng/µl to generate calibration curves. Samples were vortexed for 20 s and a 10 μl aliquot of each sample was transferred to a Pyrex reaction vial containing a 10 μl solution of 5 ng/μl sorbitol (≥98%) in HPLC-grade water as internal standard and dried under a gentle flow of N2 for 20 min.Samples containing degradation products from nuptial secretions were prepared by adding 15 μl of HPLC-water to each sample in a 1.5 ml Eppendorf tube, vortexed for 30 s and centrifuged at 8000 rpm (5223 RCF) for 5 min to separate lipids from the water layer. The water phase was transferred to a reaction vial using a glass capillary. This procedure was repeated with the remaining lipid layer and the water layers were combined in the same reaction vial containing 10 μl of a solution of 5 ng/μl sorbitol and dried under N2 for 20 min.For derivatization of sugars and samples, each reaction vial received 12 μl of anhydrous pyridine under a constant N2 flow, then vortexed and incubated at 90 °C for 5 min. Three μl of N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA; Sigma-Aldrich) was added to each reaction vial and centrifuged at 1000 rpm (118 RCF) for 2 min. Vials were incubated in a heat block at 90 °C for 1.5 hr and vortexed every 10 min for the first 30 min of incubation.The total volume of sample was ~10 μl, and 1 μl was injected into the GC-MS (6890 GC coupled to a 5975 MS, Agilent Technologies, Palo Alto, CA). The inlet was operated in splitless mode (17.5 psi) at 290 °C. The GC was equipped with a DB-5 column (30 m, 0.25 mm, 0.25 μm, Agilent), and helium was used as the carrier gas at an average velocity of 50 cm/s. The oven temperature program started at 80 °C for 1 min, increased at 10 °C/min to 180 °C, then increased at 5 °C/min to 300 °C, and held for 10 min. The transfer line was set at 250 °C for 24 min, ramped at 5 °C/min to 300 °C and held until the end of program. The ion source operated at 70 eV and 230 °C, while the MS quadrupole was maintained at 200 °C. The MSD was operated in scan mode, starting after 9 min (solvent delay time) with a mass range of 33–650 AMU.For GC-MS data analysis, the sorbitol peak area was obtained from the extracted ion chromatograms with m/z = 205, the sorbitol base peak. The area of peaks of glucose, maltose and maltotriose were obtained from the extracted ion chromatograms using m/z = 204, the base peak of the three sugars. The most abundant peaks of each sugar were selected for quantification36, and these peaks did not coelute with other peaks. Then, the peak areas of the three sugars were divided by the area of the respective sorbitol peak in each sample to normalize the data and to correct technical variability during sample processing. This procedure was performed to obtain the calibration curves and quantification of sugars in our experiments.The results of sugar analysis using GC-MS are reported in Supplementary Figs. 1–4.Analysis of nuptial secretionsWe focused the GC-MS analysis on glucose, maltose and maltotriose in WT♂ nuptial secretion (Fig. 4c). To quantify the time-course of saliva-catalyzed hydrolysis of WT♂ nuptial secretion to glucose, 1 µl of GA♀ saliva was mixed with 1 µl of 10 male-equivalents/µl. We incubated the mixtures for 0, 5, 10 and 300 s at 25 °C, and added 4 µl of methanol to stop the enzyme activity (n = 5 each treatment). Each sample contained the nuptial secretions of 5 males to obtain enough detectable amount of sugars. For the statistical analysis, the amounts of sugars were divided by 5 to obtain the amount of sugars in 1 male (1 male-equivalent). These amounts were also used for generating Fig. 4c and Supplementary Table 9. In calculations of the concentration of the three sugars (mmol l−1), the mass and volume of the nuptial secretion were measured using 70–130 male-equivalents of undiluted secretion of each strain (n = 3). The mass and volume of the nuptial secretion/male, including both lipid and aqueous layers, were approximately 30–50 µg and 40–50 nl. Because it was difficult to separate the lipid layer from the water layer at this small scale, we roughly estimated that the tergal reservoirs of the four cockroach lines had 30 nl of aqueous layer that contained sugars.To quantify the time-course of saliva-catalyzed hydrolysis of maltose and maltotriose to glucose, 1 µl of GA♀ saliva was mixed with 1 µl of 200 mmol l−1 of either maltose or maltotriose (Fig. 4d, e). Incubation time points were 0, 5, 10 and 300 s at 25 °C and methanol was used to stop the enzyme activity. Controls without saliva were also prepared using HPLC-grade water instead of saliva and 300 s incubations. n = 5 for each treatment.PhotomicroscopyThe photographs of the tergal glands and mouthparts (Fig. 5) were obtained using an Olympus Digital camera attached to an Olympus CX41 microscope (Olympus America, Center Valley, PA).Statistics and reproducibilityThe sample size and number of replicates for each experiment are noted in the respective section describing the experimental details. In summary, the samples sizes were: Mating bioassays, n = 18–80; Feeding assays, n = 16–65; Sugar analysis, n = 5; Life history parameters, n  > 14. All statistical analyses were conducted in R Statistical Software (v4.1.0; R Core Team 2021) and JMP Pro 15.2 software (SAS Institute Inc., Carey, NC). For bioassay data and sugar analysis data, we calculated the means and standard errors, and we used the Chi-square test with Holm’s method for post hoc comparisons, t-test, and ANOVA followed by Tukey’s HSD test (all α = 0.05), as noted in each section describing the experimental details, results, and in Supplementary Tables 1–11.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More