Coastal wetlands, including mangrove forests and saltmarshes, are among the most carbon-dense ecosystems worldwide. In their undisturbed state, coastal wetlands act as important carbon sinks. A large portion of the carbon captured by coastal wetlands is allocated to fine roots and stored in the soil as organic carbon. Fine roots ( More
All analysis was undertaken in Great Britain and associated islands over 20 km2. All prioritisations were undertaken at a 10 × 10 km landscape-scale on cells with greater than half land coverage. We considered designations ‘protected for biodiversity’ to be Sites of Special Scientific Interest (SSSI) and National Nature Reserves (NNR); and landscape protection designations to include National Parks (NP), Areas of Outstanding Natural Beauty (AONB), and Scottish National Scenic Areas (NSA). Different cell protection ‘cutoffs’ were tested at 30, 40, 50, 60, and 70% (Supplementary Table 1). Hence cells were considered to be ‘protected for biodiversity’ at the landscape-scale if SSSI/NNR coverage was above the percentage land cutoff, e.g. at least 40% IUCN IV protection (Fig. 1: black cells). ‘Protected landscapes’ were 10 × 10 km cells with total coverage from all of the designations above the cutoff, e.g. at least 40% IUCN V (or greater) protection, but under 40% level IV protection (Fig. 1b: grey cells). Results were qualitatively similar for all cutoffs (Supplementary Tables 2 and 3). The joint proportion of cells protected for biodiversity and protected landscapes were most similar to the actual coverage at the 40% ‘cutoff’ (27.80% of 10 × 10 km cells ‘protected’ compared to 26.71% actual area coverage), and this is presented in the main text. All designation data used is publicly available from the respective national spatial data repositories for England21 (SSSI/NNR/NP/AONB), Scotland22 (SSSI/NNR/NP/NSA), and Wales23 (SSSI/NNR/NP/AONB).We used the recorded distributions of 445 priority species listed under the Section 41 (Natural Environment and Rural Communities Act, 2006), provided by Butterfly Conservation (BC), Biological Records Centre (BRC); and breeding bird atlas data from British Trust for Ornithology (BTO)24. BTO bird atlas data are only available at the 10 × 10 km scale, which limited the spatial resolution of the analysis. We used all priority species that we were able to acquire from the above recording bodies between 2000 and 2014 (Supplementary Data 1). We used the raw distribution records for 156 species that were very localised (10 or fewer presence records) and for a further 77 species which could not be modelled (most of which were also very rare, and for which models did not converge). For the remaining 212 species with over 10 presence records, we interpolated their range using Integrated Nested Laplace Approximations (INLA) in the inlabru R package25. We used a joint model predicting distribution while accounting for recording effort, including biologically relevant covariates: seasonality, growing degree days, water availability, winter cold26, and soil pH from the Countryside Survey 2007 dataset27. These covariates were calculated from monthly means of weather data (mean temperature, sunshine and rainfall) for the decade to 2014 provided by the Met Office28. We also included soil moisture in the calculation of water availability29. We used raw data records from all 445 species, along with broad habitat layers extracted from the Land Cover Map 201530, in a Frescalo analysis31 to estimate recorder effort. See Supplementary Methods for further details of modelling.We carried out a spatial prioritisation using Core Area Zonation32, whereby cells are removed iteratively, first removing those that contribute the smallest cell value: the maximum proportion of species distributions within the remaining cells. In this way cells remaining longer within the solution complement species representation of other cells to a greater extent, and hence contribute most to underrepresented species’ distributions. However, priorities were constrained by masking or ‘locking in’ different relevant areas to each scenario such that all other cells must be removed first; reducing overall solution optimality but ensuring complementarity to masked areas. Scenario 1 only masked cells protected for biodiversity and didn’t consider other designations beyond that. Scenario 2 also masked cells protected for biodiversity but, corresponding to the 30by30 pledge, additionally masked protected landscapes.We undertook a parallel analysis additionally incorporating opportunity costs calculated from agricultural land classification and urban areas33,34,35 (Supplementary Fig. 2, Supplementary Table 4). Although urban areas are often excluded from SCP analyses, it is important to consider species complementarity of all landscapes (the government 30% target applies to the entire land surface). Since some urban/near-urban areas contain nationally rare species, we include urban areas, albeit imposing the maximum opportunity cost in these cells. In this analysis, cell value was calculated as the maximum proportion of species distributions within the remaining cells divided by the mean opportunity cost of the cell (Supplementary Fig. 3, Supplementary Tables 2 and 3).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More
Data collectionSelection of the case cities and city functional componentsTo make the research results more universal, we set the criteria for the selection of case cities as follows. (1) Large cities: cities in which the built-up area exceeded 1000 km2. We chose Beijing, Shanghai, and Tianjin. Beijing is China’s capital and political centre, Shanghai is China’s largest economic centre, and Tianjin is one of China’s four municipalities directly governed by the Central Government; (2) medium cities: cities in which the built-up area varied between 400 and 1000 km2. We chose two provincial capital cities in central China, Wuhan and Hefei, and an economically developed coastal city, Ningbo; (3) small cities: cities in which the built-up area was smaller than 400 km2. Small cities need to have a complete urban form and functions. We selected three economically developed small cities Changzhou, Nantong and Jiaxing.The selection of city functional component types should cover typical city functional components related to the coupling between humans and the city in urban systems, including production, processing, circulation, decomposition and other functions: Kentucky Fried Chicken (KFC) and McDonald’s (McD), two of the most popular western fast-food restaurants in China; Lanzhou Noodles (LZN) and Shaxian Snacks (SXS), two of the most popular Chinese fast-food restaurants in China; Agricultural Bank of China (ABC), one of the four most widely distributed banks in China; swimming pool (SP), a type of indoor sports venue popular in recent years; Shunfeng (SF) and Shentong (STO) express outlets, two of the most commonly used express service components in China; China National Petroleum Corporation (CNPC) and China Petroleum and Chemical Corporation (Sinopec) gas stations, two gas station enterprises accounting for more than half of the total number of gas stations in China; WTP, a type of waste treatment component; GH, a type of primary biological production component; and DF, a type of secondary biological processing component.Acquisition of city functional component dataLatitude and longitude data of the above city functional components were obtained through electronic maps and remote sensing images and verified through field investigation. AutoNavi and Baidu electronic maps are the two most widely used map suppliers in China due to their high accuracy and practicality46. In particular, the location of service city functional components can be accurately obtained through electronic maps. WTPs have detailed lists and location data on the government websites, and GHs can be accurately identified in Google Earth images due to their unique appearance31. Therefore, these three types of raw data are listed as the main sources of location data for functional components.Latitude and longitude data of the KFC, McD, ABC, SP, LZN, SXS, SF, STO, CNPC, Sinopec and DF locations were retrieved from AutoNavi and Baidu historical electronic maps through Python 3.5 software (https://www.python.org/). The 2012 and 2015 historical electronic map data originated from the East China Normal University Humanities and Social Sciences Big Data Platform47, and the 2018 historical electronic map data originated from the Peking University Open Research Data Platform48. Based on AutoNavi and Baidu, each individual component was strictly filtered by name and type. Please refer to the Supplementary Table S3 for a summary of the detailed filtering conditions.Accurate WTP latitude and longitude data were obtained by using the WTP name and address to query the AutoNavi map coordinate picking system [the WTP name and address were acquired from the Ministry of Ecology and Environment of the People’s Republic of China (www.mee.gov.cn), China Environment Network (www.cenews.com.cn) and Beijing Municipal Ecology and Environment Bureau (sthjj.beijing.gov.cn)]. GH latitude and longitude data were determined via a method commonly used in community ecology, which has previously been reported31. Briefly, ArcGIS 10.3 software was employed to generate grids covering the entire city (the size of each grid was 0.5 × 0.5 km), and these grids were then converted into the keyhole markup language (KML) format and imported into Google Earth for GH visual interpretation. The GHs were characterized as (a) bright white or bluish-white, (b) rectangular-shaped objects, (c) oriented in rows or separated by paths or bare areas. If a GH occurred in a specific grid, the centre of the grid was marked with the landmark tool to obtain the corresponding latitude and longitude data.Land price and housing price are affected by location factors such as population, employment, transportation, and amenities and are important indicators to determine whether a city is monocentric or polycentric49,50. Land price was also used as a determining indicator in our study. The concentric circle model was first established by Von Thünen51 to study the order of agricultural land use from urban to rural areas, and it is still an important method to explore research topics along the urban–rural gradient32,52.To obtain the land price distribution curve along the urban–rural gradient, all the standard land parcel information in each case city through the real-time land price query function provided by the China Land Price Information Service Platform (www.landvalue.com.cn), including land price, latitude and longitude, was obtained, and the parcel with highest land price was defined as the city centre. Concentric circles with an increasing radius of 1-km intervals were generated by adopting the city centre as the circle centre, and the average land price of all standard land parcels in each concentric ring was considered as the land price of the ring. We found that in all the case cities, the land price exhibited an obvious monotonous downward trend from the centre to the edge of the city (Supplementary Fig. S7). Therefore, we assumed a monocentric city model and used the concentric circles to define the urban–rural gradient.To acquire density distribution curves of the city functional components along urban–rural gradients, the latitude and longitude data of the KFC, McD, ABC, SP, LZN, SXS, SF, STO, CNPC, Sinopec, GH, WTP and DF components were applied for map labelling purposes. Concentric circles with the increasing radius of 1-km intervals were generated by adopting the city centre as the circle centre, and the number of each type of component in each concentric ring was counted. Since the overall number of WTPs and DFs was smaller, the concentric circle radius was increased at 5- and 10-km intervals, respectively, and the number of WTPs or DFs in each concentric ring was determined, while the component density in each ring was calculated by dividing the number by the area of the ring.To calculate the ecosystem services per unit area for each type of city functional component, the revenue of each component in the current year was determined. KFC and McD revenue data were retrieved from Yum China Holdings and Askci Corporation, respectively. ABC revenue data originated from the Agricultural Bank of China, Ltd., and SF and STO revenue data were acquired from SF Holding Corporation, Ltd., and STO Express Corporation, Ltd., respectively, while CNPC and Sinopec revenue data were retrieved from PetroChina Company, Ltd., and Sinopec Corporation, respectively. Moreover, LZN and SXS revenue data were obtained via field investigation. Environmental impact data of the KFC, McD, CNPC and Sinopec components originated from the Ministry of Ecology and Environment of the People’s Republic of China (www.mee.gov.cn), while LZN and SXS environmental impact data were obtained via field investigation. The costs of the KFC, McD, LZN, SXS, CNPC and Sinopec environmental impacts were converted according to the Environmental Protection Tax Law, 2018. The WTP ecosystem services were retrieved from Liu et al.53, and the GH ecosystem services originated from Chang et al.54, while the DF ecosystem services were obtained from Fan et al.55. The cultural services of all components were determined through field investigation.Data processingTo intuitively describe the density changes of city functional components along the urban–rural gradient, the density of the components in the above concentric rings were adopted as the ordinate, the distance from the city centre to the edge of the ring was adopted as the abscissa, and scatter plots were created. To compare the characteristic values of the density distribution of each type of component more clearly, a distribution model was used to fit the scatter plots35,36.Fitting of the density distribution curve of the city functional componentsThrough the nonlinear fitting function in OriginPro 2019 software (https://www.originlab.com/), the Gumbel model56,57 was considered to fit the above scatter plots to generate density distribution curves of all city functional components. The goodness-of-fit (choosing the 13 types of components in Beijing as examples) is shown in the Supplementary Fig. S2.The component density (P, individual components km−2) at a given distance from the city centre (d, km) along the urban–rural gradient is calculated as follows:$${P} = {P_{max}} {cdot} {{e^{-{e}}}^{-frac{{{d}}-{d^{*}}}{{w}} , – , frac{{{d}}-{d^{*}}}{{w}} , + , {1}}}$$
(1)
where Pmax (individual components km−2) is the peak value of the curve, d* (km) is the peak position of the curve, and w (km) is a parameter controlling the width of the curve.Calculation of the niche width of the density distribution curve of the city functional componentsTo intuitively compare the distance spanned by the density distribution curve of the city functional components, the difference in the abscissa between a density value of 10% of the Pmax value on the density distribution curve was adopted as the niche width W (km).Calculation of the skewness and kurtosis of the density distribution curve of the city functional componentsThe skewness and kurtosis are calculated according to the following equation58:$$text{skewness } = frac{frac{1}{{{n}}}{sum }_{{{i}}= {1}}^{{n}} ,{left({{x}}_{{i}}-{bar{x}}right)}^{3}}{{left(frac{1}{{{n}}}{sum}_{{{i}}= {1} }^{{n}} ,{left({{x}}_{{i}}-{bar{x}}right)}^{2}right)}^{frac{3}{{2}}}}$$
(2)
$$text{kurtosis } = frac{frac{1}{{{n}}}{sum }_{{{i}}= {1}}^{{n}} ,{left({{x}}_{{i}}-{bar{x}}right)}^{4}}{left(frac{1}{n}sumnolimits_{i=1}^{n} ,{left({{x}}_{{i}}-{bar{x}}right)}^{2}right)^2}-3$$
(3)
where xi (km) is the distance from each individual type of component to the city centre, and ‾x (km) is the average of the distances from all individual types of components to the city centre.Correlation analysis between the characteristic values of the density distribution curveLinear and nonlinear regression analyses in Microsoft Excel 2019 were implemented to study the relationship between the characteristic values of the density distribution curve, and the regression form with the best R2 value was selected.Correlation analysis between the characteristic values of the density distribution curve and the city sizeLinear and nonlinear regression analyses in Microsoft Excel 2019 were implemented to study the relationship between the characteristic values of the density distribution curve and the city size, and the regression form with the best R2 value was selected.Framework for ecosystem service assessment of the city functional componentsAccording to the classification of the Millennium Ecosystem Assessment (MA), ecosystem services include provisioning, regulating, cultural and supporting services59. In this study, the ecosystem services (goods and services) provided by the city functional components (artificial ecosystems) were divided into target and accompanied services (Supplementary Fig. S6), both of which may include provisioning, regulating and cultural services.In this study, the target services of the KFC, McD, LZN, SXS, CNPC, Sinopec, GH, and DF components were provisioning services, the target services of the ABC, SF, STO, and WTP components were regulating services, and the target services of component SP were cultural services. According to the guidance of Liu et al.53, the above regulating and cultural services were divided into positive and negative services (dis-services).The net service (NES, USD m−2 yr−1) is the sum of the positive services (target services + positive regulating services + positive cultural services) and dis-services (negative regulating services + negative cultural services):$${NES} = sum_{{i} = 1}^{n}{ES}_{i}$$
(4)
where ESi (USD m−2 yr−1) is the value of a given type of ecosystem service involved in this study, and n is the number of ecosystem service types involved in this study.The ecological index (γ) is calculated as follows:$${gamma } = {TGS}/ |EDS|$$
(5)
where TGS (USD m−2 yr−1) denotes the target services of the city functional components, and EDS (USD m−2 yr−1) denotes the dis-services of the city functional components.Calculation of the ecosystem services of the city functional componentsThe calculation methods are provided in the supplementary materials. More
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Narrowing crop diversity in the world’s food supplies is a potential threat to food security25; however, there have been few empirical studies to link crop diversity to system-level nutritional measures, especially beyond dietary intake at the household level9. Here we develop a method to link crops to specific micronutrients using a network approach and assess the role of crop production and imports on nutritional stability outcomes in 184 countries between 1961 and 2016. Similar to other scholars25,26, we find that crop diversity has increased over time in many regions, but that in many cases these gains are due to imports. Despite this increase in crop diversity, nutritional stability has remained stagnant or decreased in all regions except Asia, a trend largely attributed to our finding that gains in crop diversity coincide with fewer new nutritional links in a given food system.The general relationship between crop diversity and nutritional stability is contextualized by changes in crop degree and explains why stability does not mirror diversification trends. Improving crop diversity will always increase the size of the crop-nutrient network, but stability depends on the number and pattern of links within this network. As in other diversity–stability relationships functional identity matters, and declines in crop degree could reflect shifts toward networks with less nutrient-rich crops. For example, production-based crop diversity in Senegal increased by 29%, while crop degree dropped by 19% as the composition of its food supply shifted from staples (e.g., millet, groundnuts, sweet potatoes) to include less nutrient-dense crops (e.g., sugar cane, watermelon, cabbage). In light of on-going homogenization of crop diversity26, attaining the benefits of nutritional stability will require further understanding of the topology of crop-nutrient networks.By considering both production and nutritional diversity, our approach advances the quantification of food system resilience—the capacity over time of a food system and its units at multiple levels, to provide sufficient, appropriate, and accessible food to all, in the face of various and even unforeseen disturbances27. Our results have many implications for our understanding of nutritional measures and their relationship to crop diversity. First, our work reaffirms the existing body of research demonstrating that crop diversity is important for agricultural resilience11, and it does so at a national scale. Previous work has examined patterns of crop or nutritional diversity at global scales15,28 or linked crop diversity and nutrition-relevant outcomes at the field or landscape levels9. Our work answers recent calls8 to explore crop diversity and nutrition-relevant outcomes at a larger scale through a country-level analysis and incorporates both production and imports, the latter of which has been significant for driving an increase in the types of crops available in a given country over time. To be clear, we are measuring the relationships of crop diversity to nutrients and their susceptibility to disturbance; we are not measuring nutritional outcomes such as dietary intake, dietary diversity, or other health-related outcomes that are the result of nutrition. Just as nutritional status cannot be determined from dietary intake alone, nutritional stability does not determine the availability, let alone utilization, of nutrients. This is a natural area to expand this work moving forward.Second, our work establishes a functional relationship between crop diversity and nutritional stability. We suggest that this non-linear relationship has important implications for thinking about the types of crops grown or imported in a given region and how they ensure nutrient availability. A foundation shared by ecology and nutrition is that diversity can improve long-term functioning of complex biological systems29,30. Like other ecological diversity–resilience relationships, we observe that diversity loss can result in rapid loss of function31. In countries where diversity is already low, our results indicate that crop failures, either through production failure or an inability to import such crops, could lead to rapid reductions in nutrient availability within a country. Moreover, multiple failures of highly important regional crops, as might occur during a drought or other extreme events, could have catastrophic nutritional impact. Such countries are thus vulnerable to a variety of potential global challenges both ecological (e.g., climate change) and economic (e.g., trade wars).Third, that nutritional stability is stagnant or decreased over time in all regions but Asia highlights that increasing crop diversity—at least at the national level—does not necessarily lead to more stability. Instead, the wide variability in nutritional stability across countries highlights clear vulnerabilities both across and within regions. Africa has the greatest inter-regional variability, demonstrating that in some cases neighboring countries have very different stabilities of crop nutrients in their food supply chain in any given year. This variability is likely driven by multiple factors including the capacity of a country to trade32, in country food availability as a result of war or political/social unrest33,34,35, or exposure to climate-induced disasters36.Finally, the important role of imports in many regions highlights that crop diversity and nutritional stability are market exposed. While trade can positively affect food security37, it can also hinder nutrition efforts38 and could be a vulnerability if imports comprise a significant portion of nutritional stability for a given population. Countries with a high reliance on imports are thus subject to trade wars, market shifts, and price shocks that can occur for a variety of reasons39. Such countries may be more likely to experience increased variability in the future, especially as climate change is expected to affect agricultural production, markets, and trade40.The use of these results could help inform high-level discussions within countries and regions about the key crops for a given place and their availability via import or domestic production. Scenario development using our metric could help target country-specific crop additions that would maximize nutritional stability. Our approach could also be used to identify potential tradeoffs in production and import outcomes, at least as it relates to the availability of a given amount of nutrients in a certain place. In the context of policy interventions, this system-level metric could be applied in panel-type designs to diagnose whether initiatives (e.g., promoting or increasing food production, trade and storage) at different scales of organization (e.g., household, community, national) will effectively promote food system resilience programs41.Such potential applications also highlight the importance of identifying several caveats and important limitations. First, although we are addressing the nutrients available in a given country in a given time, we are not equating this with food security. This “availability” is only one component of food security, with access, utilization, and stability being other critical pillars. Thus, even though nutritional stability is generally high in most regions and remained stagnant (or increased in Asia), this does not mean that people are not food insecure. Adequate food and nutritional security comprises much more than the factors captured in our analysis, which provides a relative measure of nutrient availability not an absolute metric of adequacy. In the present study, we focused on nutrients available from crops, because animal-based products are rarely resolved to the species level and there is large interspecies variability in crop micronutrient composition. Animal-based products nonetheless play a critical role in providing some nutrients, thus there may be greater variability between countries when accounting for animal-based foods. There are also some methodological limitations. Crops are likely to vary in their loss susceptibility according to exogenous factors, such as market value or climate change vulnerability or pest pressure or simply abundance. In our current approach, all crops have equal removal probability; crop removal scenarios that account for these differential vulnerabilities is an exciting next step. Our current approach considers only nutrient presence or absence and may underestimate nutritional stability because ultimately the vulnerability of nutrient provision will also depend on how much of that nutrient is produced. Considering fractional crop loss or removal probabilities based on production levels could add realistic complexity in future analyses. Furthermore, complex system modeling of trade dynamics could explore to what extent import-based network re-orientation rescues nutritional stability by allowing for network rewiring via crop substitutability42,43. Finally, there are recognized shortcomings with the existing FAO data, especially in many low-income countries44. Nevertheless, to our knowledge, it is the best available data of its kind and scale available, so we utilize it knowing that there are many opportunities to improve this work moving forward.Despite these caveats, this work advances a method to assess the relationship between crop diversity and nutrient availability globally over the past 55 years. Future research could expand this work in multiple ways by combining crop-nutrient availability data with nutritional intake data to better assess whether available nutrients in the supply chain are making their way into household consumption. This would more completely link crop diversity with food and nutritional security outcomes, rather than just food availability as this work has done. Furthermore, our network tolerance method could be advanced by exploring the importance of certain crops for a given country or region by considering non-random loss of crops. Finally, with climate change expected to affect the yields of many globally important crops45 and potentially cause multiple crop failures at once36, this type of analysis could advance our understanding of food system vulnerability to specific crop failures and provide guidance on climate adaptation efforts or crop diversification strategies to safeguard against climate change.Resilience is now a central paradigm in many sectors—humanitarian aid, disaster risk reduction, climate change adaptation, social protection. Most analyses of resilience in food systems occur at household or community scales17 or focus on broader patterns of food production and distribution18,39. Erosion of biological diversity typically leads to loss of ecosystem functioning and services, likewise loss of crop diversity may to lead to potentially drastic shifts in nutritional stability. Together this and future analyses have the potential to direct the protection or restoration of crop diversity so as to best support nutrient availability that is stable to current and future challenges. More
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Polar bears in Norway have undergone a staggering loss in genetic diversity in recent decades, as a result of the decline of Arctic sea ice.
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doi: https://doi.org/10.1038/d41586-021-02438-1
References1.Maduna, S. N. et al. Proc. R. Soc. B 288, 20211741 (2021).PubMed
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We focused our research on countries/territories in Asia (specifically, Hong Kong SAR, Japan, Myanmar, Thailand and Vietnam) because COVID-19 had not spread much outside Asia at the time of data collection and the global effects were predominantly concentrated in East and Southeast Asia. Our five survey countries/territories were chosen because they all have relatively high levels of wildlife trade but also represent very different forms of trade (for example, the pet trade in Japan versus the wild-meat trade in Vietnam). Surveying respondents from markets with these different forms of trade thus allowed an examination of how the full variety of wildlife consumption types may be impacted by perceived disease risk. Budgetary constraints precluded the inclusion of further countries, although we believe those that were surveyed provide a valid snapshot of the main regional issues and patterns. The exception to this may be the exclusion of China, a key global player in the wildlife trade and the possible origin of the COVID-19 virus. Conducting research in China requires an extensive process to obtain permission that was not consistent with the opportunistic nature of our survey, which was mobilized quickly to target opinions from a snapshot view of an (at that time) emerging disease. Given the time-sensitive nature of the research, we were therefore unable to wait for the necessary permissions to include China in this survey.Our online survey was conducted between March 3–11, 2020 and surveyed 1,000 respondents in each of the five target countries/territories. We designed and translated our questionnaires with local experts to ensure questions were culturally appropriate, understandable and relevant. The survey was a quantitative data collection instrument that comprised 32 questions, lasted on average 8 minutes, and respondents were offered an incentive for participating. Respondents aged 18+ were invited via email from an online panel of over 2.5 million people in the target countries/territories, and could answer on any internet-capable device (for example smartphone, tablet, laptop) at their convenience. Only respondents aged 18 and over were eligible to take the survey, which was entirely voluntary. Any respondents working in advertising, public relations, marketing, market research or media industries were screened out to prevent possible bias. The email invite that was sent to participants did not specify the exact nature of the survey to avoid skewing the participants towards those that believed they know about the topic. Instead, the invite indicated that the questions would be about ‘consumption and shopping habits’. The panel is maintained by Toluna (https://tolunacorporate.com/), an online data collection group focused on providing high-quality market research data to clients in various business and non-business sectors. Toluna builds and maintains large online consumer panels to collect these data while adhering to stringent global and local guidelines for panel management and data quality, and is a member of the European Society for Opinion and Market Research (https://www.esomar.org).Toluna respects privacy and is committed to protecting personal data. Their privacy policy (https://tolunacorporate.com/legal/privacy-policy/) provides information on how Toluna collects and processes personal data, explains privacy rights and gives an overview of applicable legislation protecting the handling of personal information. Toluna only uses personal data when the law allows the data to be used.Respondents were asked demographic questions, and quotas based on the most recent census data for each country/territory were used to ensure the final sample profile was nationally representative of age and gender, except in Myanmar where internet access skewed online panel members to a younger male demographic. Specifically, participants were excluded once quotas on age and gender were filled, and again, participants working in advertising/public relations, marketing research or media were excluded from the survey as we believed these jobs could influence responses. Respondents were asked about societal, economic and environmental concerns, their perception of COVID-19 and their attitudes towards wildlife and wildlife consumption (Supplementary Methods). We also excluded respondents who stated that they were unsure whether they or anyone in their social circle had recently purchased wildlife products (n = 421), as well as an additional n = 39 respondents who were unable to answer survey questions that were later included as covariates in our models.Because of the potentially sensitive nature of wildlife consumption, we asked about past wildlife purchases indirectly, questioning respondents on whether anyone within their social circle, including themselves, had recently purchased wildlife products. Indirect questions can improve answer rates for questions that people may feel uncomfortable about answering honestly27. During the pandemic, respondents may have felt uncomfortable about revealing wildlife purchases, given links between wildlife consumption and COVID-19. Additionally, although most wildlife consumption is legal (with restrictions) in the markets surveyed, some is not, and researchers can be perceived as having interests contrary to that of the respondent. For less-sensitive questions on future wildlife consumption and changes in consumption resulting from COVID-19, we asked respondents for their own response rather than that of their social group.Previous studies have found a high correlation between an individual’s admission of using a wildlife product and their likelihood of being within a network of individuals who buy such products28, and suggested that this is linked to homophily in social networks, especially in Southeast Asia. The homophily principle states that people’s personal networks are homogeneous with regard to many socio-demographic, behavioural and intrapersonal characteristics29. Research on wildlife consumption in other Southeast Asian contexts suggests that social groups can be a motivator to begin or maintain consumption of wildlife products28,30. Our own previous research supports this, indicating a strong correlation between one’s own tiger and ivory purchases and knowing someone within one’s social circle who has purchased such products. Additionally and recognizing the homophily principle, behaviour change campaigns targeted at social networks rather than individuals per se are likely to achieve better results than non-targeted campaigns. Changing perceptions of acceptability is a key aspect of social marketing and is used in the social mobilization domain of social and behaviour change communications, which has become a popular framework for reducing demand for illegally traded wildlife products31. Influencing people within a wildlife consumer’s social network may therefore have a higher rate of efficacy than attempting to influence the perceptions of individuals who do not know any consumers of wildlife.We used hierarchical Bayesian regression models to assess relationships between socio-demographic explanators and our three response variables: (1) self-reported recent wildlife consumption, (2) change in wildlife consumption as a result of COVID-19 and (3) anticipated future wildlife consumption. Explanatory variables included 22 non-collinear variables in six categories: basic demographics, awareness and level of worry of COVID-19, COVID-19 personal impacts, support for and effectiveness of wildlife market closures, international travel habits and general attitudes towards global issues (Supplementary Table 1). Aside from household income (measured in US dollars per year), age (midpoint of year categories from the survey question) and education (ordinal, reflecting increasing level of schooling), all other variables were categorical; those with more than two categories were collapsed into dummy variables. Income, age and education were standardized and included to investigate whether a person’s general socio-economic status affects wildlife consumption. General attitudes towards global issues were expected to reflect aspects of respondents’ political tendencies, while travel habits were included to test the hypothesis that those who travel internationally more habitually are, and will be, more frequent consumers of wildlife. Questions regarding awareness and impacts of COVID-19, and concern about future disease epidemics, were asked to determine how the pandemic may be shaping wildlife consumption. Finally, support and perceived effectiveness of wildlife market closures were included as predictor variables since this measure has been suggested as a strong policy lever to reduce wildlife consumption.The general structure of all three models was as follows:$$y_{ij}sim {{{mathrm{Bernoulli}}}}left( {theta _{ij}} right)$$
(1)
$${mathrm{logit}}left( theta right) = alpha + {{u}_1} + {beta} {mathbf{X}} + {{u}_2}{mathbf{Z}}$$
(2)
This model allowed both coefficients and intercepts to vary across countries (that is, a ‘random-slope random-intercept’ model). In equation (1), yij is whether or not individual i in country j reported wildlife consumption, modelled as a Bernoulli trial with probability θij. The logit transformation of θ (equation 2) is a linear function of parameters α and u1 (the fixed intercept term and a vector of the country-specific intercept terms, respectively), as well as a vector of fixed regression coefficients β and a vector of country-specific regression coefficients u2, with X and Z being the corresponding design matrices32. For α and β, we used an improper flat prior over the real numbers, while the group level parameters u1 and u2 were assumed to arise from a multivariate normal distribution with mean 0 and unknown covariance matrix. The covariance matrix was parameterized by a correlation matrix having a Lewandowski–Kurowicka–Joe prior, and a standard deviation with half-Student t prior with three degrees of freedom32.For the three dependent variables, we evaluated the predictive power of a model containing all 22 variables, as well as six subset models, using Watanabe–Akaike Information Criterion and leave-one-out cross-validation33. Each of these six subset models contained all explanatory variables except for those within one of the six categories described above (for example, all explanatory variables except those relating to international travel habits, all explanatory variables except those relating to support for wildlife market closures). We used this model-comparison approach to test whether any of these categories of explanatory variable were more or less important in explaining wildlife consumption; if particular categories of variable are stronger predictors of wildlife consumption, this could help inform where future conservation interventions should focus on. Watanabe–Akaike Information Criterion and leave-one-out cross-validation are both measures of model predictive accuracy (both use log predictive density as the utility function or comparison metric) and have been suggested as useful metrics for Bayesian model selection33. We interpreted variable coefficients whose 95% Bayesian credible intervals did not contain 0 as providing strong evidence for the impact of that variable on the outcome in each of the three models for self-reported wildlife consumption (that is, recent, future and changes due to COVID-19). Models were estimated using the R statistical computing software34, in particular the package brms32, with four chains of 1,000 iterations each, a 500-iteration warm-up period, and with successful convergence verified by confirming that R-hat statistical values were less than or equal to 1.01 (ref. 22).We used the Bayesian hierarchical model of anticipated future wildlife consumption and generated predicted probabilities of future consumption for our sample population (Fig. 2, grey bars). We then predicted future consumption probabilities for a hypothetical behaviour-change intervention (Fig. 2, coloured bars). This intervention was simulated by setting the ‘medical impact’ variable to zero for all individuals, and by assigning all individuals into the ‘aware lots’ and ‘support very likely’ categories for questions related to level of awareness of COVID-19 and level of support for government closure of domestic wildlife markets, respectively. All other variables for individuals were held at the levels recorded in the surveys. We considered the difference between these two predicted probabilities as the impact of the hypothetical behaviour-change intervention, which we examined at the level of the country/territory and within education, age, income and gender demographic classes. Strong evidence for the effectiveness of this hypothetical intervention among countries and demographic classes was suggested where Bayesian credible intervals around the mean predicted difference were less than zero (Supplementary Table 3).Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More
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