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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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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

Mammalia Linnaeus, 175829.Artiodactyla Owen, 184830.Ruminantia Scopoli, 177731.Infraorder Tragulina Flower, 188332.Family Lophiomerycidae Janis, 198733.Included generaLophiomeryx, Zhailimeryx, Krabimeryx, Chiyoumeryx nov. gen.Genus Krabimeryx Métais, Chaimanee, Jaeger, and Ducroq, 200117.EtymologyKrabi—from Krabi Basin, where the fossils were found, and—meryx is the Greek word for ruminant.Diagnosis [modified after Métais et al.17]Small primitive ruminant with lower molars morphologically close to those of Zhailimeryx. Krabimeryx differs from Zhailimeryx in: more laterally compressed lingual cuspids in the lower molars; an entoconid displaced to anterior with respect to the hypoconid; the lack of both a paraconid and a hypoconulid in m1 and m2; a p4 with a mesolingual conid that is located more posterior and less individualized; a p4 without a distinct posterolingual conid. Krabimeryx differs from Lophiomeryx by less selenodont labial cuspids in the lower molars, the presence of a developed external postmetacristid, and by a distinct groove on the anterior side of the entoconid, the entoconidian groove. Krabimeryx can be distinguished from Iberomeryx in having a well-marked entoconidian groove; the lack of a clear external postprotocristid; the third lobe of m3 not forming a complete buckle; and a more transversely compressed hypoconulid in the m3. Krabimeryx possesses a huge notch in lingual view between the entoconid and the third lobe in the m3.Type speciesKrabimeryx primitivus Métais, Chaimanne, Jaeger, and Ducroq, 200117.Included speciesKrabimeryx gracilis nov. comb. (Miao, 198220).Krabimeryx gracilis nov. comb. (Miao, 198220).Figure 1A and Figure S1.Figure 1Dentition of Krabimeryx gracilis nov. comb. (Miao, 1982)20 (A, B, G, H), Chiyoumeryx nov. gen. shinaoensis (Miao, 1982)20 (C, D), Chiyoumeryx nov. gen. flavimperatoris nov. sp. (E) and Iberomeryx miaoi nov. sp. (F–I). Krabimeryx gracilis nov. comb. (Miao, 1982)20: (A) IVPP V 6546-1 (holotype), partial skull with right and left M1–M3; (B) IVPP V 6546-2 (holotype), right fragmented mandible with m2–m3. Chiyoumeryx nov. gen. shinaoensis (Miao, 1982)20: (C) IVPP V 6531 (holotype), right mandible with p2–m3 and tooth socket of p1; (D) IVPP V 6532 (paratype), right fragmented maxillary with P4-M3. Chiyoumeryx nov. gen. flavimperatoris nov. sp.: (E) IVPP V 6547 (holotype), right mandible with p4–m3; Iberomeryx miaoi nov. sp.: (F) IVPP V 6551 (holotype), left mandible with m1–m3 (mirrored); (G) lower molar Lophiomerycidae dental nomenclature (based on the m3 of IVPP 6546-2): 1 internal postmetacristid, 2 metaconid, 3 external postmetacristid, 4 internal preentocristid, 5 entoconidian groove, 6 external preentocristid, 7 entoconid, 8 posthypoconulidcristid, 9 hypoconulid, 10 prehypoconuldicristid, 11 posthypocristid, 12 hypoconid, 13 prehypocristid, 14 ectostylid, 15 postprotocristid, 16 protoconid, 17 preprotocristid, 18 anterior cingulid; (H) upper molar Lophiomerycidae dental nomenclature (based on the M2 of IVPP 6546-1): 1 postmetacrista, 2 metacone, 3 premetacrista, 4 mesostyle, 5 postparacrista, 6 paracone, 7 paraconid labial groove, 8 preparacrista, 9 parastyle, 10 preprotocrista, 11 anterolingual cingulum, 12 protocone, 13 postprotocrista, 14 entostyle, 15 additional cone, 16 premetaconulecrista, 17 metaconule, 18 postmetaconulecrista; (I) lower molar Tragulidae dental nomenclature (based on the m2 of IVPP V 6551, reversed): 1 metaconid, 2 external postmetacristid, 3 Dorcatherium fold, 4 internal postmetacristid, 5 preentocristid, 6 entoconid, 7 postentocristid, 8 posterior cingulid, 9 posthypocristid, 10 hypoconid, 11 prehypocristid, 12 ectostylid, 13 external postprotocristid, 14 Tragulus fold, 15 internal postprotocristid, 16 protoconid, 17 preprotocristid, 18 paraconid, 19 preparacristid. (J) phylogenetic position and stratigraphie of the Shinao/Yangjiachong/Xiaerhete ruminants (topology2). a stem Ruminantia, b Archaeomeryx, c Chiyoumeryx nov. gen. and Krabimeryx gracilis, d crown Ruminantia, e Iberomeryx miaoi nov. sp.; 1 lingual view, 2 occlusal view. Scale bare is 1 cm.Full size image*v pars1982 Lophiomeryx gracilis—Miao: 532, Table 3, Figs. 6 and 720.v non1982 Lophiomeryx gracilis?—Miao: 536, Fig. 820.v pars1987 L. gracilis—Janis: 21133.v pars1997 L. gracilis—Vislobokova: Fig. 321.v pars2000 L. gracilis—Guo, Dawson, and Beard: 247, Table 214.v pars2001 L. gracilis—Métais, Chaimanee, Jaeger, and Ducroq: 239, 24117.v pars2012 L. gracilis—Mennecart: 6234.NeodiagnosisKrabimeryx gracilis has an m2 that is wider than the m3; this is the other way round in K. primitivus. Moreover, the entoconid is less anterior relative to the hypoconid in K. gracilis than it is in K primitivus. The ectostylid is large in K. gracilis, while it is absent in K. primitivus. The cingulum on the upper molars in K. gracilis is more developed than in K. primitivus.HolotypeIVPP V 6546, partial skull with right and left M1–M3 (IVPP V 6546-1) and an associated right fragmented mandible with m2–m3 (IVPP V 6546-2) found in occlusion with the skull.Additional materialIVPP V 6549, right m3 on fragmented mandible; IVPP V 6550 left fragmented mandible with m1–m2; IVPP V 26638, right m1. Measurements are given in Table S1.LocalitiesShinao Basin, Panxian County, Southwestern Guizhou, China; Xiaerhete locality, Jiminay County, Xingjiang, China. Late Eocene.Taxonomical attributionThe herein described specimens were first attributed to the genus Lophiomeryx20. However, the thorough reassessment of the specimens now leads to the conclusion that Lophiomeryx gracilis sensu Miao20 contains three different species and genera, but none of them can be assigned to Lophiomeryx.Based on the presence of a strong lingual cingulum in upper molars and a short anteroposteriorly oriented postprotocrista, as well as the absence of a premetacristid and an anterior fossa widely open in the lower molars, we can conclude that the specimens, IVPP V 6546-1, IVPP V 6546-2, IVPP V 6549, and IVPP V 6550, belong to Lophiomerycidae or Tragulidae35,36. However, the absence of a large paraconid and the absence of an elongated external postmetacristid distinguish the specimens from primitive Tragulidae17,36. In Zhailimeryx jingweni, the cuspids are more slender than in the herein described specimens14, a feature the taxon shares with K. primitivus. In Z. jingweni, m1 and m2 are of relative similar width14, while in K. primitivus and the herein described specimens from Shinao the m2 is clearly bigger than the m117. Similarly to K. primitivus, the herein described specimens differ from Z. jingweni in its lower molar lingual cusps being more laterally compressed, and in an entoconid that is slightly shifted to anterior with respect to the hypoconid, while it is more posterior in Z. jingweni14,17. Furthermore, K. primitivus and the herein described specimens from Shinao both lack the rudimentary paraconid present in Z. jingweni14,17.Like K. primitivus, the here-described specimens differ from Chiyoumeryx nov. gen. (described below) and the Lophiomeryx species L. mouchelini, L. chalaniati and L. angarae by having more massive and more bunomorph lower molars16,17,24,34,37. Furthermore, Zhailimeryx jingweni, K. primitivus, and the herein described specimens differ from Lophiomeryx by the presence of a developed external postmetacristid and by a distinct entoconidian groove on the anterior side of the compressed entoconid14,17. In Lophiomeryx, the back fossa of m3 is widely open due to the strong reduction of the posthypoconulidcristid34,37. In contrast to this, Krabimeryx primitivus possesses a clearly developed posthypoconulidcristid forming a buckle on the m3 back basin17, similarly to the specimens from Shinao described here.Summing up, the general morphology of the teeth in the herein described specimens is most similar to the one observed in K. primitivus. They both share a similar huge notch in lateral view between the third lobe of m3 and the entoconid and the entoconidian groove, features that clearly distinguishing them both from Lophiomeryx and Zhailimeryx. Thus, we attribute the specimens IVPP V 6546-1, IVPP V 6546-2, IVPP V 6549, and IVPP V 6550 to the genus Krabimeryx. However, significant differences occur with the type species, ruling out the synonymisation of K. gracilis nov. comb. and Krabimeryx primitivus. While both species are very similar in size, K. primitivus has an m3 wider than m2, while it is the converse for K. gracilis nov. comb. Moreover, the entoconid is less shifted to the anterior with respect to the hypoconid in K. gracilis nov. comb. than in K primitivus. There is no ectostylid in K. primitivus, while it is large in K. gracilis nov. comb., forming a transverse cristid between the protoconid and the hypoconid. The cingulum on the upper molars is more developed in K. gracilis nov. comb. than in K. primitivus.Due to these differences we decided to create the new combination Krabimeryx gracilis nov. comb.Chiyoumeryx nov. gen.ZooBank LSIDurn:lsid:zoobank.org:act:464C46E0-5A69-4AC1-A9DD-8A7DF76D5CC0.EtymologyChiyou is a tribe leader of the ancient China, about 5–4 k years ago. Chiyou’s tribe was believed to be in relation with the peoples in southern China; -meryx means ruminant in Greek.DiagnosisChiyoumeryx nov. gen. differs from Zhailimeryx and Krabimeryx notably by the absence of the entoconidian groove. The lower teeth are more laterally compressed in Chiyoumeryx nov. gen. and the metaconid is linguo-labiallly more central than in the two other genera. The posthypoconulidcristid in the lower molars of Chiyoumeryx nov. gen. is longer than in Krabimeryx and its p4 is posteriorly extended, while this part is reduced in Krabimeryx. Chiyoumeryx nov. gen. differs from Lophiomeryx by the shape of the mandible. In Chiyoumeryx nov. gen. there is no diastema between p1 and p2 and the diastema between c and p1 is extremely reduced. The outline of the mandible in occlusal view is relatively straight in this species. Lophiomeryx possesses a long diastema between c and p1 and a small one between p1 and p2, as well as a regularly curved occlusal outline of the corpus. The lower premolars of Chiyoumeryx nov. gen. are laterally compressed giving a more elongated aspect to these teeth than in Lophiomeryx. The trigonid is smaller than the talonid in m1 and m2 in Chiyoumeryx nov. gen. and the preprotocristid terminates centrally and does not reach the lingual side. In Lophiomeryx the trigonid and talonid are of similar size and the preprotocristid is longer and reaches the lingual side. Moreover, in Chiyoumeryx nov. gen., the posthypoconulidcristid is longer than in Lophiomeryx. The shape of the P4 in Chiyoumeryx nov. gen. differs from the one in Lophiomeryx: the posterolingual crista does not meet the posterolabial crista.Type speciesChiyoumeryx nov. gen. shinaoensis (Miao, 198220).Included speciesChiyoumeryx nov. gen. flavimperatoris nov. sp.; ?Chiyoumeryx nov. gen. turgaicus (Flerow 193838).Chiyoumeryx nov. gen. shinaoensis (Miao, 198220).Figure 1B and Figure S2.*v1982 Lophiomeryx shinaoensis—Miao: 530, Table 3, Figs. 3–520.v1987 Lophiomeryx shinaoensis—Janis: 203, 204, 211, 212, Fig. 8B33.v1997 Lophiomeryx shinaoensis—Vislobokova: Fig. 321.v2000 L. shinaoensis—Guo, Dawson, and Beard: 247, Table 214.v2001 L. shinaoensis—Métais, Chaimanee, Jaeger, and Ducroq: 239–241, 24117.v2012 L. shinaoensis—Mennecart: 6234.NeodiagnosisChiyoumeryx nov. gen. shinaoensis is bigger than Chiyoumeryx nov. gen. flavimperatoris nov. sp. but smaller than ?Chiyoumeryx turagicus. The transversely oriented anterior conid in the p4 in Chiyoumeryx nov. gen. shinaoensis differs from the obliquely oriented one in Chiyoumeryx nov. gen. flavimperatoris nov. sp. In Chiyoumeryx nov. gen. shinaoensis, the posterolingual conid is vestigial on p4. Chiyoumeryx nov. gen. shinaoensis has no anterior cingulid, while in Chiyoumeryx nov. gen. flavimperatoris nov. sp. there is a tiny anterior cingulid. Chiyoumeryx nov. gen. shinaoensis possesses lower crowns than ?Chiyoumeryx nov. gen. turgaicus. Chiyoumeryx nov. gen. flavimperatoris nov. sp. possesses an ectostylid, which is absent in ?Chiyoumeryx nov. gen. turgaicus.HolotypeIVPP V 6531, right mandible with p2–m3 and tooth socket of p1.ParatypeIVPP V 6532, right fragmented maxillary with P4–M3.Additional materialIVPP V 6533, right mandible with p2–m3 and tooth socket of i1–p1; IVPP V 6534, left fragments mandible with m1–m3; IVPP V 6535, right fragmented mandible with m1–m3; IVPP V 6536, left fragmented mandible with p4–m3; IVPP V 6537, right fragmented mandible with p4–m2; IVPP V 6538, left p4; IVPP V 6539, right maxillary with P3–M3; IVPP V 6540, right maxillary with P4–M2; IVPP V 6541, right maxillary with M2–M3; IVPP V 6542, left maxillary with P3–M1; IVPP V 6543, right maxillary with M1–M3; IVPP V 6544, Left M3; IVPP V 6545, left maxillary with P4–M3. Measurements are given in Table S1.LocalityShinao Basin, Panxian County, Southwestern Guizhou, China. Late Eocene.Taxonomical attributionMiao20 attributed the here described specimens to the genus Lophiomeryx assuming that these fossils belong to a traguloid. “Lophiomeryx” shinaoensis clearly is a Lophiomerycidae: anterior and posterior fossae are open on the lower molars due to the absence of a premetacristid and the extreme reduction or absence of a postentocristid, there is no external postprotocristid, there is a mesolingual conid on the p4, the symphysis of the mandible extends backward up to the p12,36. It also shares with undisputable Lophiomerycidae a reduced posthypoconulidcristid that does not enclose the third lobe lingually.“Lophiomeryx” shinaoensis differs from Zhailimeryx and Krabimeryx in the absence of the entoconidian groove14,17. Moreover, the teeth are more laterally compressed in “Lophiomeryx” shinaoensis and the metaconid is linguo-labially more centeral14,17. The posthypoconulidcristid in “Lophiomeryx” shinaoensis is more elongated than in Krabimeryx and its p4 has an extended posterior part, while it is reduced in Krabimeryx17.Contrary to what was suggested by Métais and Vislobokova2, Miomeryx altaicus24 is currently known only by its holotype, which is an upper tooth row (AMNH 20383, see Matthew and Granger24). Comparable to M. altaicus, the postprotocrista reaches the premetaconulecrista on the M2 in “Lophiomeryx” shinaoensis. These two cristae fuse totally on the M3 in the here described specimens. However, even if both genera also bear a very strong cingulum, “Lophiomeryx” shinaoensis clearly differs from M. altaicus in having broader and squarer molars and straighter lingual cristae in the P4.Miao20 compared the here revised fossils with the seven Lophiomeryx species considered valid at that time. Unfortunately, very few specimens document most of these species and there is considerable doubt considering the genus attribution of most of them34,36,37,38,39. In any case, we agree with Miao20 (p. 535) that “L. [= Praetragulus] gobiae is readily distinguished from other known Lophiomeryx species as well as from L. shinaoensis by the absence of p1, the anterior flange of metaconid joining protoconid crescent.”. Miao20 (p. 535) already noticed that “Lophiomeryx chalaniati, Lophiomeryx gaudry [= Iberomeryx minor], and Lophiomeryx benarensis are radically different from the present specimens in the anterior branches of the protoconid crescent [= preprotocristid], of m1 and m2 not reaching the lingual border while the posterior branches of hypoconid crescent [= posthypocristid], doing so”. “Lophiomeryx” shinaoensis shares this condition with the Mongolian Lophiomeryx angarae24. However, the trigonid is smaller than the talonid on m1 and m2 in “Lophiomeryx” shinaoensis and the preprotocristid ends in the labio-lingual axis of the molars, while trigonid and talonid are of more similar width combined with a longer preprotocristid in the European Lophiomeryx species and L. angarae16,34,37. The shape of the P4 in “Lophiomeryx” shinaoensis is very different from Lophiomeryx (see Brunet and Sudre37, Figs. 4 and 6). In Lophiomeryx, the posterolingual crista fuses with the posterolabial crista. In “Lophiomeryx” shinaoensis, the curved posterolingual crista does not join the distal end of the posterolabial crista but reaches the labial side. Furthermore, “Lophiomeryx” shinaoensis clearly differs from L. angarae L. mouchelini, and L. chalaniati in the shape of the mandible. These three species of Lophiomeryx possess a very elongated diastema between c and p1 and a small one between p1 and p224,36,37. As part of the genus diagnosis, Mennecart34 (p. 62 and p. 67), adapted from Brunet and Sudre37 and Métais and Vislobokova2, noticed that “the corpus mandibulae presents [in Lophiomeryx: L. angarae, L. mouchelini, and L. chalaniati24,34,37] a concave ventral profile just behind the mandible symphysis, then it becomes regularly convex until the beginning of the ramus, where there is a rounded incisura vasorum. […] On the anterior part of the mandible there are two foramen mentale.” Moreover he wrote that the “p1 is always reduced and leaf-like, separated from c and p2 by diastemata.” (Mennecart34, p. 67). In “Lophiomeryx” shinaoensis there is no diastema between p1 and p2 and the diastema between c and p1 is extremely reduced. The p1 is relatively big considering the root size. The lower outline of the mandible in lateral view is relatively straight. “Lophiomeryx” shinaoensis shares these characteristics with “Lophiomeryx” turgaicus40. Miao20 (p. 535) already noticed strong similarities between “Lophiomeryx” turgaicus and “Lophiomeryx” shinaoensis. The lower premolars of “Lophiomeryx” turgaicus and “Lophiomeryx” shinaoensis are strongly laterally compressed and the p4 is rectangular, giving the lower premolar toothrow an more elongated aspect than in L. angarae, L. mouchelini, and L. chalaniati20,24,30,38,40. Moreover, in these two species, the posthypoconulidcristid is of similar length, longer than in L. angarae, L. mouchelini, and L. chalaniati.Based on these observations, we can assume that “Lophiomeryx” shinaoensis and “Lophiomeryx” turagicus cannot be assigned to the genus Lophiomeryx and may both belong to the same new Lophiomerycidae genus that we here name Chiyoumeryx nov. gen. Chiyoumeryx nov. gen. shinaoensis differs from ?Chiyoumeryx nov. gen. turgaicus nov. comb. in being lower crowned, smaller, possessing an ectostylid, having the symphysis starting under p1, and a shorter diastema.Chiyoumeryx nov. gen. flavimperatoris nov. sp.Figure 1C and Figure S3.v1961 cf. Miomeryx sp.—Xu: 316, 323, 32426.v pars1982 Lophiomeryx gracilis—Miao: 532, Table 3, Fig. 9a,b20.v non1982 Lophiomeryx gracilis?—Miao: 536, Fig. 820.1983 Lophiomeryx sp.—Wang & Zhang: 122, 12741.v1983 cf. Miomeryx sp.—Wang & Zhang: 12341.v1997 Miomeryx sp.—Vislobokova: Fig. 321.v pars1997 L. gracilis—Vislobokova: Fig. 321.v1999 cf. Miomeryx sp.—Zhang, Long, Ji, & Ding: 7, Table 527.v pars2000 L. gracilis—Guo, Dawson, and Beard: 247, Table 214.v pars2001 L. gracilis—Métais, Chaimanee, Jaeger, and Ducrocq: 239, 24117.v2007 Miomeryx sp.—Métais and Vislobokova: 1942.v pars2012 L. gracilis—Mennecart: 6234.ZooBank LSIDurn:lsid:zoobank.org:act:1DF6F58C-F08B-4657-BD4A-7C597653926F.Etymologymeaning yellow (flavor-) emperor (imperatoris) in latin. Chiyou fought with the Yellow Emperor, the ancestor of Chinese, but was defeated.DiagnosisChiyoumeryx nov. gen. flavimperatoris nov. sp. shows the above-mentioned characteristics of the genus. Chiyoumeryx nov. gen. flavimperatoris nov. sp. is smaller than Chiyoumeryx nov. gen. shinaoensis and ?Chiyoumeryx nov. gen. turgaicus. The p4 of Chiyoumeryx nov. gen. flavimperatoris nov. sp. differs from Chiyoumeryx nov. gen. shinaoensis by an oblique anterior conid, which is labio-lingually oriented in the larger species. A very short posterolingual conid is located between the posterolabial cristid and the transverse cristid in the p4 of Chiyoumeryx nov. gen. flavimperatoris nov. sp., while it is absent on Chiyoumeryx nov. gen. shinaoensis. In Chiyoumeryx nov. gen. flavimperatoris nov. sp., there is a tiny anterior cingulid, while it is absent in Chiyoumeryx nov. gen. shinaoensis.HolotypeIVPP V 6547, right mandible with p4–m3 (previously attributed to Lophiomeryx gracilis20).ParatypeIVPP V 6548, left mandible with p4–m3 (previously attributed to Lophiomeryx gracilis20).Additional materialIVPP V 2600, left p4–m2 (previously attributed to cf. Miomeryx sp.26). Measurements are given in Table S1.LocalitiesYangjiachong locality lying in the Caijiachong marls, Qujing, Yunnan, China; Shinao Basin, Panxian County, Southwestern Guizhou, China. Late Eocene.Taxonomical attributionIVPP V 6547 and IVPP V 6548 from Shinao were previously attributed to Lophiomeryx gracilis20, while IVPP V 2600 from Caijiachong marls was first described as cf. Miomeryx sp.26. All these specimens share the same size and dental morphology, and originate from a similar stratigraphic position. That is why we attribute them to the same species.None of these specimens can be attributed to Krabimeryx or Zhailymeryx, as the entoconidian groove is absent14,17. Furthermore, the external postmetacristid is more marked in the considered specimens than in Krabimeryx and Zhailymeryx, forming a deep groove. The third basin is also very different in the here-described specimens from Krabimeryx and Zhailymeryx: the third lobe is a little tilted parallel with the prehypoconulidcristid and posthypoconulidcristid. The back fossa of m3 is very narrow.Furthermore, the here-described specimens can be distinguished from K. gracilis (previously attributed to the same species), by a smaller size and a slenderer shape. The ectostylid is smaller than in K. gracilis. The anterior cingulid in the lower molars is stronger in K. gracilis than in the here-considered specimens. The small postentocristid (especially on m3) of the here-described specimens is absent in K. gracilis.The here-described specimens possess all characteristics in the lower molars that are typical for Chiyoumeryx nov. gen. and distinguish this genus from Lophiomeryx24,34,37. Furthermore, as in Chiyoumeryx nov. gen. shinaoensis, the p4 is laterally compressed giving it a more elongated aspect than in Lophiomeryx24,34,37. Therefore, we consider it justified assigning the here-described specimens to Chiyoumeryx nov. gen. However, they differ from Chiyoumeryx nov. gen. shinaoensis in as smaller size and the morphology of the p4: (1) the anterior conid is oblique while it is labio-lingually oriented in Chiyoumeryx nov. gen. shinaoensis. (2) There is a tiny anterior cingulid that is absent in Chiyoumeryx nov. gen. shinaoensis. (3) There is no additional cristid on the mesolingual conid, which is a well-rounded conid, while in Chiyoumeryx nov. gen. shinaoensis, there is a short posterolingual cristid. (4) The posterolingual conid stands between the posterolabial cristid and the transverse cristid, while in Chiyoumeryx nov. gen. shinaoensis, the posterolingual conid is very small and oblique between the transverse cristid and the posterior stylid and does not join the posterolabial cristid. Due to these distinct differences we erect a new species: Chiyoumeryx nov. gen. flavimperatoris nov. sp.Family Tragulidae Milne-Edwards, 186442.Genus Iberomeryx Gabunia, 196443.Diagnosis (modified from Mennecart et al.36)Small-sized ruminant with upper molars possessing the following combination of characters: well-marked parastyle and mesostyle in small-column shape; strong paracone rib; metacone rib absent; metastyle absent; unaligned external walls of metacone and paracone; strong postprotocrista stopping against the anterior side of the premetaconulecrista; continuous lingual cingulum, stronger under the protocone. Lower dental formula is primitive (3–1–4–3) with non-molarized premolars. Tooth c is adjacent to i3. Tooth p1 is single-rooted, reduced and separated from c and p2 by a short diastema. The premolars have a well-developed anterior conid. Teeth p2–p3 display a distally bifurcated mesolabial conid. Tooth p3 is the largest premolar. Tooth p4 displays no mesolingual conid and a large posterior valley. Regarding the lower molars, the trigonid and talonid are lingually open with a trigonid more tapered than the talonid. The anterior fossa is open, due to a forward orientation of the preprotocristid and the presence of a paraconid. The internal postprotocristid is oblique and the external postprotocristid reaches the prehypocristid. The internal postprotocristid, postmetacristid and preentocristid are fused and Y-shaped. Protoconid and metaconid display a weak Tragulus fold and a well-developed Dorcatherium fold, respectively. The mandible displays a regularly concave ventral profile in lateral view, a marked incisura vasorum, a strong mandibular angular process, a vertical ramus, and a stout condylar process.Type speciesIberomeryx parvus Gabunia, 196443 from Benara (Georgia), late Oligocene44.Included speciesI. minor45, Iberomeryx miaoi nov. sp.Iberomeryx miaoi nov. sp.Figure 1D and Figure S4.v 1982 Lophiomeryx gracilis?—Miao: 536, Fig. 820.ZooBank LSIDurn:lsid:zoobank.org:act:EE3F88E9-0EAF-4EC6-A46F-8623241E614B.DiagnosisIberomeryx with a very large paraconid, which is smaller in Iberomeryx minor and Iberomeryx parvus. The metastylid is not strong but is more developed than in the other species. The ectostylid is big on m1, smaller on m2 and absent on m3, while I. minor displays an ectostylid on all molars and I. parvus none at all. Iberomeryx miaoi nov. sp. is of similar size to I. minor and its m2 is smaller than the one of I. parvus. It differs from I. minor by a thin anterior cingulid. Moreover, its protoconid is positioned slightly more anterior than in I. parvus. The molars appear to be more massive and bulkier in this species than in I. minor and I. parvus.HolotypeIVPP V 6551, left mandible with m1–m3 (only specimen known). m1 5.1 × 3.5, m2 5.2 × 4.1, m3 8.0 × 4.0.EtymologyWe dedicate this species to Prof. Miao Desui who was the first to describe the Shinao fauna.Locality and horizonShinao Basin, Panxian County, Southwestern Guizhou, China. Late Eocene.Taxonomical attributionThis minute ruminant was referred to Lophiomeryx gracilis? by Miao20. However, he already noticed that the size of this individual was smaller than in the other specimens attributed to Lophiomeryx gracilis. Miao20 excluded an attribution of IVPP V 6551 to “Lophiomeryx” gaudryi due to a closed posterior section of the posterior fossa on the m3. However, in both teeth, the posterior fossa is still open by the reduction of the postentocristid.The here-described specimen clearly differs from Lophiomeryx by the presence of an external postmetacristid forming a slight Dorcatherium fold, a developed external postprotocristid (clearly visible at least on m2), and a large paraconid36. Furthermore the external postprotocristid and prehypocristid are connected on their distal ends and the third basin of m3 forms a well-formed buckle, unlike the condition in Lophiomerycidae14,16,33,36,37. The combination of these characters is typical for Tragulidae36.Very few taxa are so far known in the early evolution of the Tragulidae. Only Archaeotragulus, Iberomeryx, and Nalameryx are recognized as potential Paleogene Tragulidae17,36,46, of which Archaeotragulus is currently the oldest representative described17,47. Archaeotragulus possesses lower molars with a broadened talonid in comparison to the trigonid and displays an entoconidian groove36. In the case of IVPP V 6551, the trigonid and talonid are of similar size and no specific entoconidian groove can be observed. Mennecart et al.36 considered Nalameryx a Tragulidae notably based on the presence of the M structure (the external postmetacristid, the internal postmetacristid, the internal postprotocristid, and the external postprotocristid are interconnected forming a M in occlusal view), including the Tragulus fold and Dorcatherium fold, and the absence of a rounded mesolingual conid in the p435. IVPP V 6551 differs from Nalameryx in having an m3 wider than m1 and similar m1 and m2 widths17. In size proportions and molar morphology, IVPP V 6551 resembles the genus Iberomeryx. In IVPP V 6551, the relative size of the m2 is more similar to I. minor. In Iberomeryx minor, the anterior cingulid is big36,46, while in Iberomeryx parvus the cingulid is thin48 like in IVPP V 6551. The teeth of IVPP V 6551 appear to be more massive and bulkier than in I. minor and I. parvus36,48. Similarly to I. minor, the protoconid of IVPP V 6551 is a little more anterior than in I. parvus36,48. IVPP V 6551 clearly differs from I. parvus and I. minor by the presence of a very large paraconid, which is smaller in the two other species36,48. Moreover, the metastylid in IVPP V 6551 is slightly more developed than in I. minor and not present in I. parvus43,48. Iberomeryx minor displays an ectostylid on all molars36, while this structure is absent from I. parvus48. The ectostylid in IVPP V 6551 is large on m1 to absent on m3. Based on these differences we decided to erect the new species Iberomeryx miaoi nov. sp.Origin of crown Ruminantia and dispersal pattern of Paleogene Eurasian ruminantsSo far five families and 13 genera of Ruminantia are known during the middle and late Eocene in Eurasia2,18,19. Based on molecular data, the origin of crown ruminants should be searched for between the latest late Paleocene (56.5 Ma) and the latest early Oligocene (29 Ma)49,50. With the description of stem Tragulidae from the early Oligocene of Western Europe (Iberomeryx) and the late Eocene from Southern Thailand (Archaeotragulus)17, Mennecart et al.26 and Mennecart and Métais51 verified that the oldest crown ruminants date back at least to the latest Eocene (34 Mya). The presence of the tragulid genus Iberomeryx in Shinao, Southern China, further confirms this and may actually represent the oldest fossil of a Tragulidae known and thus of a crown Ruminantia (37–35 Mya, Fig. 1), since no Pecora is known during the Eocene so far51.The here presented reassesment of the Shinao ruminants in combination with literature data reveals a clear pattern in the distribution of Eocene ruminants. Among Archaeomerycidae, Archaeomeryx and Miomeryx are found in Northern and Central Asia [Kazhakstan, Mongolia, and northern part of China2,21,53 (see Fig. 2)]. The lophiomerycid Lophiomeryx (as Lophiomeryx angarae) as well as the Asiatic Praetragulidae (Praetragulus) occupy the same area2. The Mongolian Lophiomeryx angarae is most likely closely related to the European species Lophiomeryx mouchelini. Due to the strong morphological similarities, some specimens of L. mouchelini were actually first described as Lophiomeryx cf. angarae54. Lophiomeryx mouchelini or its ancestors arrived in Europe with the Grande-Coupure dispersal event at the Eocene–Oligocene transition ca. 34 Mya ago (oldest European records: Calaf, Spain, MP22; Möhren 9, Germany, MP21-22; age comprised between the German localities Haag2 MP21 and Möhren 13 MP2234,37,53). The close relationship of these European and the Mongolian species confirms that the origin of the Grande-Coupure cohort may be deeply anchored in the Eocene of Central-Northern Asia (Fig. 2).Figure 2Paleobiogeography of the Eurasiatic ruminants during the Eocene at the genus level. The localities are from the synthesis of data2,17,18,22,49. The palinspastic map is modified from Scotese52.Full size imageThe Southern part of Asia presents a totally different ruminant community at the genus level and includes the Archaeomerycidae Indomeryx and Notomeryx, the Lophiomerycidae Krabimeryx and Chiyoumeryx nov. gen., the Bachitheriidae Bachitherium and the Tragulidae Archaetrogulus and Iberomeryx2,17,18,19,21,53 (see Fig. 2). The oldest Bachitherium is currently known from the Balkan area during the Eocene18,19. The Tethys Ocean separated this area from Western Europe until its progressive disappearance during the Oligocene, ca. 31 Mya55,56. Bachitherium and a cohort of rodents (Pseudocricetodon, Paracricetodon, and the Melissodontinae)19 did not reach Western Europe prior to the opening of this passage. Similarly to the genus Bachitherium, Iberomeryx arrived in Western Europe after the drying out of the Tethys Ocean ca. 31 Mya, during the Bachitherium dispersal event18,19,36. Iberomeryx is mainly known from the middle early Oligocene of Western Europe34,36,57 and the late Oligocene of Anatolia and Georgia43,48,58. Discovering Iberomeryx in the Eocene of Eastern Asia confirms an Asiatic origin of this genus. The close relationship between South-eastern Europe and South-eastern Asia is furthermore supported by anthracotheriids (extinct artiodactyls related to hippopotamids) and rhinocerotoids59,60.Mennecart et al.18,19 proposed that mammals originating from Asia arrived in Western Europe during the early Oligocene in two faunal events: the Grande-Coupure, ca. 33.9 Mya and the Bachitherium dispersal event, ca. 31 Mya. These two faunal events imply two different and diachronous ways of dispersal. The fact that Eocene taxa from South-eastern Asia did not arrive in Western Europe prior to 31 Mya indicates that the Bachitherium dispersal Event cohort might be deeply anchored in the Eocene of Southern Asia (Fig. 2), while genera recorded from the Eocene of Central Asia are known to have arrived already during the Grande-Coupure and thus originated from a different palaeobiogeographic province. The Grande-Coupure was a dispersal event using a Northern way over the closed Turgai Strait and probably originating from Central Asia (Fig. 2). The Bachitherium dispersal event is a stepwise story with a first dispersion from Southern Asia to South-eastern Europe along the Southern path (Fig. 2) and then the dispersal throughout Europe thanks to the closure of the Tethyian Ocean18,19.The south-eastern part of Asia has shown very few changes from a warm and humid climate and environment since the Eocene4, while Northern Asia underwent a transition from warm and humid subtropical environments during the Eocene to steppe environments in the Pliocene, e.g.3,4,5. In this light it is not surprising that an increasing number of paleontological and geological studies indicate that Asia had already experienced a strong latitudinal environmental zonation during the middle and the late Eocene, e.g.6,13.These different climatic and environmental conditions in Central and South Asia led to two distinct palaeobiogeographical provinces clearly traceable in assemblages of herbivores like ruminants that was already apparent during the Eocene. The Central Asian ruminants were living in a more arid environment than the ones from South-eastern Asia (see Fig. 2). The tropical and wet environments from the South-eastern Asia led to the emergence of the Tragulidae (Iberomeryx and Archaeotragulus) and of the anthracotheriids. 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