Philippa Kaur

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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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Study areaThis study was conducted in Jilin Province, which is located in the center of Northeast China (40°52ʹ N–46°18ʹ N, 121°38′ E–131°19ʹ E) and covers an area of approximately 187,400 km2, with an elevation varying from 5 m to 2,691 m (Fig. 1). The study area has a temperate continental monsoon climate and is climatologically humid, semi-humid, and semi-arid from the southeast to the northwest. The annual mean temperature and annual total precipitation form a southeast-northwest gradient; the eastern part is relatively humid and rainy while the western region is dry in the summer months. Generally, 70–80% of the annual precipitation occurs from June to September, with the most abundant rainfall in the east. The long-term average annual temperature and average annual rainfall are 5.8 °C and 687.0 mm, respectively49. Crop cultivation is mostly concentrated in the black soil region50. The soil types of cultivated lands mainly include black soil, sand, and paddy soil, which are suitable for potato growth.Figure 1Spatial distribution of 51 meteorological stations and soil sampling sites in the study area. Soil data were divided into two categories. Soil samples (I): soil mechanical compositions, involving 81 sampling points; soil samples (II): soil physico-chemical properties, involving 79 sampling points. The map was created using ArcGIS v. 10.4.1 (http://www.esri.com/software/arcgis).Full size imagePotato growth is highly dependent on temperature and light. Jilin Province, as one of the main potato-producing areas in China, possesses sufficient sunlight and exhibits large temperature difference between day and night. Generally, potato cultivation occurs from April to May, depending on the lowest temperature (5 °C), and potatoes are harvested from August to October of the same year. Among potato production areas, mid-late maturing cultivars (e.g., Yanshu No. 4, Atlantic, Jishu No. 1, and Summer) account for about 70%, while early maturing cultivars (e.g., Favorita, Youjin, and Fujin) account for 30%51.DataClimate dataClimate data were obtained from the National Meteorological Information Center, China Meteorological Administration (http://data.cma.cn), including 51 national standard meteorological stations in Jilin Province (Fig. 1). The meteorological data contain daily average temperature, daily maximum temperature, daily minimum temperature, daily sunshine hours, and daily precipitation during 1957–2018. Based on the periods of potato sowing and harvesting in Jilin Province, the climate data between April 1 and September 31 each year were selected. To avoid the impact of extreme weather within a single year on the inter-annual climate change, we used 5-year moving average values of climate data rather than single-year values to establish a geo-climate model using regression analysis and evaluated changes in suitable areas for potato cultivation under the influence of climate change.Topography dataTopography data were extracted from the digital elevation model (DEM) sourced from the geospatial data cloud SRTM (http://www.gscloud.cn). Through a series of processes such as adding X–Y axis, splicing, vector data layering, filtering, cropping, and resampling of raster data on the ArcGIS platform, digitized elevation model (90-m resolution) maps were used to derivate layers such as longitude, latitude, slope, and aspect (Fig. 1).Soil dataSoil mechanical composition data (81 sampling sites) were extracted from the National Science and Technology Infrastructure Platform (http://soil.geodata.cn) and soil physico-chemical property data (79 sampling sites) were provided by the Soil and Fertilizer General Station of Jilin Province (http://www.jltf.cn). The sequence number of the occurrence layer is 1, and the thickness is about 20-50 cm. The soil properties extracted included contents of soil sand, silt, and clay, pH, and contents of nutrients such as organic matter (OM), quick-acting potassium (QAK), available nitrogen (AN), and available phosphorus (AP) (Fig. 1; Tables S1-S2).The soil data were rasterized using kriging. First, the soil mechanical composition data were converted into spherical coordinates, and then ordinary Kriging interpolation was used to spatialize the soil mechanical composition data. co-kriging was used to interpolate spatialize the soil physico-chemical property data. Due to limited soil samples and the lack of a continuous dataset in the study area, the soil data in 2018 were selected as a fixed background for the analysis.Analysis of climatic factorsWe used six climatic factors in this study. Usually, potatoes have has different requirements for light, heat, and water in each growth and development stage. We used average daily temperature during the growth period (ADT/°C, mean of daily average temperature from April 1st to September 30th) and active accumulated temperature ≥ 10 °C (AAT/°C d, sum of active accumulated temperature ≥ 10 °C from April 1st to September 30th) from 1961 to 2018 to reflect the temperature conditions of potato growth52,53,54,55,56. ADT at 14–17 °C was evaluated as “Most suitable”; 10–14 °C or 17–20 °C as “Suitable”; 8–10 °C or 20–24 °C as “Sub-suitable”; < 17 °C or > 24 °C as “Not suitable” for potato growth in the study area. AAT for mid-late maturing varieties at 2000–3000 °Cd was evaluated as “Most suitable”; 1,500–2,000 °Cd or 3,000–6,000 °Cd as “Suitable”; 1,300–1,500 °Cd or 6,000–8,000 °Cd as “Sub-suitable”; < 1,300 °Cd or > 8,000 °Cd as “Not suitable”.The average temperature in July (ATJ, mean of daily average temperature in July) and the day/night temperature difference from July to August (DIF/°C, mean of the day/night temperature difference from July 1st to August 31st) are the key climatic factors for the expansion of potato chunks, which have significant correlation with the meteorological yield of potato 53–57. ATJ at 16–20 °C was evaluated as “Most suitable”; 15–16 °C or 20–24 °C as “Suitable”; 12–15 °C or 24–28 °C as “Sub-suitable”; < 10 °C or > 28 °C as “Not suitable”. DIF at 8–12 °C was evaluated as “Most suitable”; 5–8 °C as “Suitable”; 2–5 °C as “Sub-suitable”; < 2 °C as “Not suitable” in the study area.During the growth and development of potato, there is a great demand for water, especially from the budding stage to the swelling stage of potato growth, which are extremely sensitive to water supply52,53,54,55,56. The total precipitation during the growth period (PP/mm, sum of the daily precipitation from April 1st to September 30th) at 700–900 mm was evaluated as “Most suitable”; 600–700 mm or 900–1,200 mm as “Suitable”; 500–600 mm or 1,200–1,500 mm as “Sub-suitable”; < 500 mm or > 1,500 mm as “‘Not suitable” in the study area.Short daylight and appropriate high temperature during the seedling stage are beneficial to promote potato root development, forming strong seedlings and increasing potato formation52,53,54,55,56. The total sunshine duration during potato growth (SD/hours, sum of the daily sunshine duration from April 1st to September 30th) at 900–1,200 h was evaluated as “Most suitable”; 700–900 h or 1,200–1,500 h as “Suitable”; 400–700 h or 1,500–1,800 h as “Sub-suitable”; < 400 h or >1,800 h as “Not suitable”.MethodsFirst, climatic factors were simulated using geo-climate models. Then, the AHP-PCA model was employed for suitability evaluation, and the satellite-based gridded environmental data were applied for suitability mapping. Finally, the degree of changes in climatic factors and suitable geographic ranges were calculated. These data were interpolated into the surface grid data with a spatial resolution of 0.03° × 0.03° (~3 km × 3 km)57,58. All maps and statistical analyses were generated using ArcGIS 10.4.159 and R 3.6.360.Geo-climate model buildingTopographic factors such as longitude, latitude, and altitude dominate the distribution of climate factors, and directly affect the solar radiation budget and atmospheric circulation, which makes the climate resources to demonstrate obvious spatial differences in both vertical and horizontal directions61,62. Based on the meteorological data and geographic information of each meteorological station, we established geo-climate models and used them to calculate the climate distribution of the study area. The difference between the highest temperature and the lowest temperature from July 1st to August 31st was used to calculate the grid layer of DIF. The relationship between climate zoning indicators and geographic factors is expressed as follows:$$ F = fleft( {lambda ,varphi ,h} right) + varepsilon $$
(1)
where, F is the simulated value of grid point of the climate zoning index; λ, φ, and h represent longitude (°), latitude (°), and altitude (m), respectively; f (λ,φ,h) is called climatological equation of regionalization index; and ε is the influence of local small topography and random factors on climate (i.e., comprehensive geographical residual term).Residual correction : Affected by local topography and random factors, the variation of climatic factors is random, which will cause errors in the calculation of geo-climate models. Therefore, the inverse distance weight (IDW) routine in ArcGIS was used to derive the simulated value of the comprehensive geographical residual term ε raster63. The interpolation calculation formula is:$$varepsilon ={sum }_{i=1}^{n}frac{{varepsilon }_{i}}{{d}_{i}^{k}}/{sum }_{i=1}^{n}{d}_{i}^{k}$$
(2)
where, ε is the simulated value of the grid point of the residual term of climatic factors; (n) is the number of meteorological stations; ({varepsilon }_{i}) is the residual value of the climate factor of the (i)-th meteorological station; ({d}_{i}) is the Euclidean distance between the grid point and the (i)-th meteorological station; k is the power of the distance.AHP-PCA and GIS based suitability analysis for potato cultivationThe suitability map for potato cultivation was generated based on identified criteria that are relevant to the climatic, soil environmental, and geophysical conditions considered. Details of the data analysis procedure, model application, and suitability classification are described as follows.

AHP-PCA model
Analytical Hierarchy Process (AHP) is a multi-criterion decision-based approach developed for analyzing complex decisions involving multiple criteria38,64,65. Principal Component Analysis (PCA) is a multivariate statistical data analysis technique that combines all input variables using a linear combination into a number of principal components that retain the most variance within the original data to identify possible patterns or clusters between objects and variables. In this study, we used AHP to calculate the weight of each zoning indicator in the evaluation index system66,67, and then, we explored the comprehensive relationship of suitability evaluation factors using the grid calculator and PCA tool on the ArcGIS platform. The first principal component will have the greatest variance, the second will show the second most variance not described by the first, and so forth. In most cases, the first three or four raster bands of the resulting multiband raster from principal components tool will describe more than 95% of the variance, that is, the cumulative contribution rate of the principal component reaches more than 95%. The variance of the weighted original data becomes larger, leading to more scientific and reasonable evaluation results. In summary, the proposed approach is achieved as follows (Fig. 2):
Step 1: The weight of each index was calculated by using AHP and consistency test;
Step 2: The indicators were standardized using the Z-Score method;
Step 3: The weights calculated in Step 1 were loaded onto the standardized indicators;
Step 4: A standardized matrix was built and the correlation coefficient matrix was calculated;
Step 5: The principal components was filtered and determined;
Step 6: The score for each principal component was calculated;
Step 7: A comprehensive score for all indicators was obtained.

Establishment of indicator system and calculation of weight
The assessment of climate change impacting suitability of potato cultivation has multiple objectives and levels. This paper combined comprehensive and hierarchical principles, relevant literature reviews38,39,40,68, expert opinions, and characteristics of potato cultivation in Jilin Province to establish an index system for evaluation of ecological environment impact, including 18 evaluation indicators: ADT ( °C d), AAT ( °C), PP (mm), SD (h), ATJ ( °C), DIF ( °C), elevation (m), slope (°), aspect (°), hill shade, sand (%), silt (%), clay (%), OM (g/kg), pH, QAK (mg/kg), AN (mg/kg), and AP (mg/kg). These indicators were classified into three categories: climatic conditions, soil environments, and topography (Table 1).
The weight of each evaluation indicator was determined by AHP. According to relevant literatures and expert opinions, we established a judgment matrix for these evaluation indicators. Pairwise comparison was used for obtaining the relative importance score between different indicators. The consistency of pairwise importance scales is one of the important measurements for successful decision-making by AHP, which could be checked using consistency ratio (CR). If CR < 0.10, the degree of consistency is satisfactory, whereas, CR > 0.10 indicates an inconsistency63,69 (Table 1).

Classification and mapping for suitability of potato cultivation

Figure 2Diagrammatic flow of the Analytical Hierarchy Process (AHP) weighted Principal Component Analysis (PCA) model evaluation process.Full size imageTable 1 Weights of all criteria used for estimating suitability of potato cultivation in the study area.Full size tableThe natural breakpoint method in ArcGIS was employed to classify lands of the study area in terms of cultivation suitability. The study area was delineated into 4 zones: zone 1 (Not suitable), zone 2 (Sub-suitable), zone 3 (Suitable), and zone 4 (Most suitable) (Table 2).Table 2 Dimensionless grading of evaluation values of potato cultivation suitability.Full size tableAfter normalizing all indicators, the cultivation suitability index was established as follows:$$I={sum }_{i}^{n}{W}_{i}{X}_{i}$$
(3)
where I is the suitability index for comprehensive evaluation, ({W}_{i}) is the weight of the indicator, ({X}_{i}) is the value after dimensionless treatment of the indicator, i is the comprehensive evaluation value of topography, climatic conditions, and soil environments. The larger the topography value was converted into a negative value for the calculation as the greater its value, the higher its negative impact on cultivation suitability. Meanwhile, the greater the pH value is, the more unfavorable the comprehensive evaluation of soil will be; the pH value was therefore inversed for the calculation.Trends and fluctuations in changes of climatic factors and suitable areasThe fluctuations of various climatic factors over the past 58 years were analyzed by coefficient of variation (CV), which was calculated as CV = (standard deviation/mean) × 100%. Temporal trends in changes of climatic factors and suitable areas were calculated using ordinary least squares linear regression on annual data from 1961 to 2018. Among them, the trend in suitable area changes was calculated based on each grid. The significance of trends was estimated following a method that considers the temporal autocorrelation by reducing the effective sample size of the time series70. And the significance of temporal trends was tested at P < 0.171. More

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As a wildlife-conservation biologist studying climate change, I want to understand the evolving environment through the eyes of large animals. My work — usually in cold, remote places — involves finding animals, and ways to eat, sleep and be warm. I might be miserable, but I get insights that others cannot into what animals are doing.For about 15 years I’ve been interested in musk oxen (Ovibos moschatus), social herd animals that roamed with woolly mammoths. This picture was taken on Wrangel Island, off the northeast coast of Russia, when I was studying how musk oxen react to polar bears. Because polar ice is melting, more polar bears are hunting on land, and they’re known to have killed musk oxen. These herd animals typically don’t flee from predators such as grizzly bears. They tend to form huddles instead, and male musk oxen have killed grizzlies. Would they try to kill polar bears, too?To find out, I dressed as a polar bear, pulling a bear head on and placing a cape over a range finder, camera and data books. I was cold and nervous. I didn’t want to be killed by a charging musk ox — or by anything else. If some oxen charged, I’d throw off my costume and stand up straight, as I’m doing here; so far, that had stopped them. I’d also encountered a female polar bear with newborn cubs, but she’d left me alone. This picture is from the end of a session, and I’d lived another day. Whew!I learnt that musk oxen are more likely to flee from polar bears than from grizzlies. But during this trip to Russia, I was arrested — over a date error on my permits. In court, the only word I understood was ‘CIA’. I was let go, but banned from returning for three years, so I’m now studying the huemul (Hippocamelus bisulcus), an endangered species of deer that lives in the shadows of glaciers at the tip of South America. As glaciers recede, how will huemul populations respond?

Nature 597, 296 (2021)
doi: https://doi.org/10.1038/d41586-021-02429-2

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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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