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Synthesis and data extractionData were collected using a literature search of Web of Science for peer-reviewed journal articles published between 1970 and November 2019. We conducted two sets of searches to capture grazing with discrete comparisons (e.g., grazed/ungrazed, moderate vs. heavy intensity grazing) and a range of grazing intensities. The search terms used for each were as follows 1) (graz* OR livestock) AND (exclosure* OR exclusion OR exclude* OR ungrazed OR retire* OR fallow* OR fence* OR paddock*), 2) (“grazing intensity” OR “grazing gradient” OR “stocking rate” OR “rotation*grazing”). Our synthesis includes domesticated and wild grazer species, with the latter defined as an undomesticated species naturally occurring in the study area during the study. Wild grazers are typically native species to the region (e.g., the American bison in Western North America) but can include non-native species that are naturalized in the area (e.g., feral horses on Sable Island).We excluded any study that did not test the effect of grazing animals. A grazer was defined using the definition provided by the authors of the respective study to account for the proportion of forage types in a herbivore’s diet that varies between seasons and habitats. For example, we included animals where their diet is assumed to come from all (e.g., cattle, sheep), most (e.g., wapiti, kangaroos), or some (e.g., deer species) grass species. However, within the included studies, these animals were classified as grazers as most of their diet was grass for the duration of the study. For added clarity about the herbivore composition in each study, we extracted a list of any herbivores listed in the paper regardless of foraging type or if any data was provided.We only included studies that measured animal diversity or abundance as a response variable and included data we could extract or contact the author to obtain9. We included any study with a grazing treatment and included observations within these studies of any grazed and ungrazed sites. All studies with grazing included a comparison to either ungrazed sites, different grazing practices (e.g., cattle vs. sheep), and/or differences in intensities (e.g., heavy/light, extensive/intensive). Studies that only measured plants or soil biota were excluded because syntheses of grazing effects on these groups have already been conducted7,11,12, and our goal was to provide a robust inventory of animal diversity. However, if a study included plants, lichens, or fungi in addition to animals, we included this data. Studies discussing marine grazing or aquatic systems were also excluded. From these preliminary filters, we identified 3,489 published manuscripts. We reviewed these 3,489 published articles and found 245 studies that surveyed animals in grazed sites. In total, we extracted 16,105 observations for over 1,200 species.We extracted 28 variables that focus on management systems, assemblages of grazer species, ecosystem characteristics, and survey type (Table 1). The latitudes, longitudes, and elevations of each study were included when provided for use with geospatial data. In addition, we included variables about the study site’s disturbance history, including last time grazed, if a flood event or fire had occurred, if fertilization was used, if the area was open or fenced off, and if the area was publicly or privately owned. Furthermore, the timeline for the study (i.e., the years the authors initiated and completed the study) was also provided. Study initiation was described by the authors and could include when the grazing treatment started, another treatment was applied, and/or animal surveys began. These timeline columns can be useful in identifying long-term studies and differentiating single grazing events or multi-year experiments. Finally, we generalized the characteristics of the ecosystem of the sites used in each study based on the climate and dominant vegetation.Table 1 The attributes and description of the metadata.csv file that lists the general characteristics of each study.Full size tableWithin the grazing data, we included information about the grazer when provided, including any measurement of the intensity of grazing (e.g., animals per hectare, the height of residual vegetation). We also provided two columns that detailed whether the study tested grazing effects using a discrete comparison or gradient of intensities (Table 2). The value for the target specimens extracted may represent either a single observation or a summarized statistic (e.g., mean animals per site). We identify unique observations as “count” and summarized statistics by the metric used, such as mean, median, standard deviation (column stat in grazingData.csv). When possible, we also included any record of other grazers that co-occurred with the observed grazer species. The data for these variables were extracted from the papers by a single researcher who read through each paper and filled in available data on the mentioned variables.Table 2 The attributes and description of the grazingData.csv file that has the extracted data from each study.Full size tableWe extracted information about the target specimen, site, year, experimental replicate, and response estimate (Table 2). We included multiple categorizations of the target species to assist future users in synthesizing similar taxa (Table 2). When a species name or genus was provided, we conducted a search query (see detailedTaxa.r) through the global biodiversity information facility (GBIF.org) to determine the taxonomic classification of the species, including kingdom, phylum, order, class, and family. When a species name was not included, we provided the lowest taxonomic resolution available. We also included a broader classification of ‘higherTaxon’ to distinguish plants, fungi, vertebrates, and invertebrates. These columns may help group similar species together for community-level analyses. Lastly, we included the characteristic of the plant community (i.e., planted or self-assembled, tilled, and its vegetation class) when plant data was reported.Patterns among studiesMost of the studies took place in the United States (26%), Australia (9%), and the United Kingdom (7%) (Fig. 1). As expected, most studies were conducted in grasslands (n = 206), followed by forests (n = 92) and shrublands (n = 82) (Fig. 2). We included publications from the entire range of years (i.e., 1970–2019), but most were published after 2000 (76%). The number of sites in a study and the study duration showed a bimodal distribution with a long tail (Fig. 3). Most studies included one to eight different sites, and few were conducted longer than five years (Fig. 3). A few studies were highly replicated, while many were limited in their replication (Fig. 3).Fig. 1The locations of studies that measured the response of animals to domestic or wild grazing.Full size imageFig. 2The number of grazing studies conducted in ecosystems around the world. We generalized the characteristics of the ecosystem of the sites used in each study based on the climate and dominant vegetation community. We separated grassland communities into those that were (a) semi-natural without recent cultivation or seeding (self-assembled), (b) recently cultivated or had supplemental seeding (planted/cultivated), and (c) a combination of both. In most grasslands, the cultivation history was unclear.Full size imageFig. 3The number of independent sites surveyed and the duration of each study. Most studies were conducted at either a single site or with some replication (e.g., 6–8 sites). Similarly, most studies were either conducted in one year ( >30%) or over a few years (e.g., 3–6 years). Very few studies (32) or lasted longer than 15 years.Full size imageSite and management data were not reported in all studies, as found in other reviews of grazing impacts on ecological processes10. Of the studies that mentioned the ownership status of the land used, 46% were on private land, 42% were on public land, and 12% had a history of both public and private ownership. Most studies included binary comparisons (56%) of grazed vs. ungrazed plots or sites, though some also included a discrete (22%) or a continuous estimate of grazing intensities (18%).Of the studies that reported plant community origin, 76% were self-assembled, 17% were planted communities, and the remaining included sites were a combination of the two. Domesticated grazers as the focal herbivore made up 67% of the studies, with 12% of the studies having wild grazers as the focal herbivore, and 21% having both present. Domesticated livestock were the most frequently surveyed grazers including cattle (n = 164), sheep (n = 83), and horses (n = 21), but studies are included that examined wild grazers, such as kangaroos (n = 6), elephants (n = 5), and pronghorn (n = 5) (Fig. 4).Fig. 4The frequency in which a study reported herbivores. We included any mention of herbivores regardless of being a grazer, browser, granivore, or other class. This list was obtained by the text within the manuscript and is different than the representation of species in the database (i.e., the measured species).Full size image More

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Study sitesThe sample site Tei Tong Tsai is located within the Island District (112°5’ E, 22°5’ N Hong Kong, China) and connected to Lantau Country Park. The rich woods in Tei Tong Tsai provide a suitable environment for insects to survive, with rich biodiversity. Weather records (Supplement 1) for May 2019 show that the highefst temperature was 27.2 °C, the lowest was 15.7 °C, the average was 21.7 °C; and the annual average rainfall was 297.8 mm. The suitable temperature and rainfall have created a suitable ecological environment and high biodiversity, establishing Tei Tong Tsai as a prime location for studying beetle diversity. In May 2019, a 13 sample sites were selected for beetle collection (Fig. 1). All latitude and longitude formats were converted to degrees, minutes, and seconds.Fig. 1Sampling points for the three passive acquisition methods used in the Tei Tong Tsai sampling site (indicated by red dots).Full size imageExperimental protocolIn this study, three passive collection methods were used for beetle collection. FIT is an efficient collecting method for insects with strong flying abilities and was first developed and used abroad14. MT and PT collect insects that are not strong flyers and live on the surface. A flight interception trap, a malaise trap, and 10 pitfall traps were set up to collect beetles in each sample site. Samples were selected to cover ecological environments at different longitudes, latitudes, altitudes, and distances from water sources. Reasonable sampling distances (depending on the terrain, with an interval between 100 and 200 m) were set up between sample sites to fully cover Tei Tong Tsai’s habitats. Due to the topography, the distance between the 10th and 11th sample points was about 350 m. The distance between two other close sample points were in the range of 100–200 m. All three traps were based on the original device to maximize the advantages and achieve better collection results.Collection devices. The flight interception trap (Fig. 2a) mainly comprises an interceptor screen (plastic net, PVC plastic glass, or plexiglas) and an insect specimen receiver (PVC), which is an efficient collection device for intercepting and collecting insects with strong flight ability. The detailed installation steps include the following: Firstly, punch two holes on the long side of the PVC screen with a hole puncher spaced about 30 cm apart; then, fix the screen to a bamboo pole with silk, install the specimen receiver, fix all three, bolt the rope, and fix it in the air with a thick rope (the sink is about 0.5–1 m from the ground). After installation, relevant drugs were placed inside the specimen receiver to poison the insects. The drugs used depend on the purpose of the study. For morphological studies, saline (5 mmol/L NaCl solution) or water with detergent is used. By contrast, DNA molecular studies use a mixture of 2% SDS (sodium dodecyl sulfate) and EDTA (ethylene diamine tetraacetic acid, 0.1 mol/L, PH = 8) or highly concentrated alcohol, which effectively controls the degradation of DNA. Currently, high-concentration alcohol, SDS and EDTA mixtures are commonly used. The device is widely applicable and can be installed in almost any habitat; however, it is best installed along the insects’ flight paths, including roads, rivers, or creeks between valleys. In this experiment, we improved this device by increasing the size of the water trough considering the actual situation of the sample site. Also, to properly conduct the molecular experiments, the reagents we used were a mixture of SDS and EDTA. Therefore, the improved device was more suitable for diverse habitats, and the insect species collected were abundant, reflecting good collection practices14.Fig. 2Three passive acquisition methods: (a) flight interception trap; (b) malaise trap; (c) pitfall trap.Full size imageMalaise traps (Fig. 2b) are large tent-like structures constructed from thin mesh. They are among the most commonly used static non-attractant insect traps and insect collection devices. Invented by Malaise (1937) and later improved upon by Townes and Sharkey, these traps are important tools for insect collection and monitoring worldwide15. The malaise trap used at the Tei Tong Tsai Country Park was the Townes type, which is generally set up in forest areas with rich habitats and relatively stable ground. The material is usually meshed mosquito netting fabricated into a tent-shaped insect interception field. The insects hit the net vertically, continue to fly upward, and are gradually led into the trap by the tilted top. The drug in the trap is usually anhydrous ethanol, which intercepts beetles with weak flying abilities16,17.The pitfall trap (Fig. 2c) is an effective method for capturing surface beetles; it is simple to use, easy to carry, and a common device for collection in the wild. The PT is created by digging a pit into the ground with the same depth as a wide-mouth plastic cup (20 cm high, 10 cm in diameter); The upper edge of the cup must be flushed with the soil surface, and a mixture of absolute ethanol is poured inside to collect flightless beetles14. About one-quarter of the way from the top, small holes are punched above the wide-mouth cup to prevent the loss of specimens from rainwater filling the cups. The 10 sets of traps in this experiment were not evenly distributed, but they were all in suitable habitats.Specimen samplingThe sampling site for this study was Tei Tong Tsai, and the sampling period was from 1st May to 28th May (2019). FIT, and PTs were collected once every two days. Due to the small number of beetles collected by MT, mt was collected only once. All beetles were picked out and arranged separately after collection, added to anhydrous ethanol, preserved, and labeled. The beetles collected by the three passive acquisition methods were picked according to morphological species.Specimen identificationThe taxonomic status for the family level of all samples was determined based on the relevant literature18,19,20,21. Relevant experts completed further identification (Supplement 2).All the specimens collected in this study are currently in the zoological museum of the Institute of Zoology, Chinese Academy of Sciences (Beijing, China).Specimen photographyBeetles were poured from the bottle and arranged separately according to the general species. Firstly, we used tweezers or a brush to place the beetles on unbreakable and unwrinkled paper (as far as possible with the backside upwards to keep them tight and neat, reducing the space left, and considering the label in the photograph). Simultaneously, we captured multiple photos according to the size and species of insect for the large specimens in the tube, adjusted the light near them to brighten the background, placed graph paper next to the beetles as a reference scale, then adjusted our Olympus camera settings to the appropriate photographing parameters. Finally, we inserted the photographed beetles and matching labels back into the tube and added anhydrous ethanol for preservation (Fig. 3). The labels were set in the photos as 2019 DTZ-FIT/MT/PTX-5XX-5XX (-N), in which 2019 represents the collection time, DTZ represents Tei Tong Tsai, FIT/MT/PT signifies the collection method, X represents the number of sampling points, 5XX-5XX represents sampling time, and N represents the photo number. If a sample site had many insects on the same date and required more than one photo, n was used to represent the number of photos. See the Supplement 3 for the complete document.Fig. 3Examples of beetles collected from three passive acquisition methods: overall photos of beetles collected by (a) FIT, (b) PT, and (c) MT. On the bottom right corner shows scale in each photo.Full size imageAfter the morphological data of the samples were collected, their Latin name and collection information were recorded in a table. Each passive acquisition method corresponded to a table, and each table was divided into 13 sheets according to 13 sampling points. The collection time was listed horizontally on each sheet, and the beetles’ species names were listed vertically (were named in the morphological species order as 1, 2, 3, …, N). The number of beetles was recorded in the corresponding position and the Supplement 4 file.Finally, data show the beetles’ biodiversity collected from each sampling site. Firstly, we summarized the data from each sampling point after completing the data statistics. Afterward, we counted the number of beetle individuals collected under the different passive acquisition methods at different points (Fig. 4). In Fig. 4, red, blue, and green represent the number of beetle individuals collected by MT, PT, and FIT, respectively. Fig. 4 shows that MT collected fewer beetles than FIT and PT. Secondly, the data of 13 sampling points in each collecting method were summarized to obtain the total number of families and species collected by each method (Fig. 5). A graph created in Excel 2016 displays the collection method as the horizontal coordinate and the number as the vertical coordinate. In the graph, red represents the number of families, and blue represents the number of species. Fig. 5 shows that FIT collected more beetle species and individuals than PT and MT, and MT collected the least. Thirdly, all data from the 13 sampling points and the three collection methods were summarized. The number of species collected in all families was counted. Families with more than ten species were selected (a total of 11 families) for data presentation (Fig. 6). Finally, a graphic was drawn in Excel 2016. Fig. 6 shows that the number of species in Staphylinidae, Curculionidae, and Chrysomelidae accounted for a large number, and the diversity was relatively high.Fig. 4Data table of numbers of individual beetles collected by different methods at 13 sampling points. The red, blue, and green columns represent the number of beetles collected by MT, PT, and FIT, respectively.Full size imageFig. 5The number of beetles collected by different passive acquisition methods. Horizontal coordinates represent collection methods. The red column and blue column represent the number of beetles collected on the family level and species level, respectively.Full size imageFig. 6Families with more than ten species (a total of 11 families) were selected for presentation. The sample sizes of each groups were also shown.Full size image More

Wheat production and yield vis-à-vis climate trendsWheat is currently grown in all six continents except Antarctica. The leading producers include China, the Russian Federation, Ukraine, Kazakhstan (RUK), India, USA, France, Canada, Pakistan, Germany, Argentina, Turkey, Australia, and United Kingdom (Fig. 1 and Supplementary Table 1). The total grain production of these twelve countries is estimated at 600 megatons (2019 data), which accounts for over 78% of the global wheat production. The top three producers are China with 133.6 megatons per year (Mt y−1), RUK with 114.1 Mt y−1, and India with 103.6 Mt y−1. RUK contains the largest harvested area of 45.8 million hectares, followed by India with 29.3 million hectares and China with 23.7 million hectares (Fig. 1A). Despite a relatively small harvested area of 10.1 million hectares (only 22% of RUK’s harvested area), the United Kingdom, France, and Germany account for the world’s highest yields per hectare, with 8.93 tons ha−1, 7.74 tons ha−1, and 7.40 tons ha−1, respectively (compared with the world’s average yield of only 3.2 tons ha−1), accounting for a total yearly production of 79.9 Mt y−1.Figure 1Global wheat area and trends in wheat yield and climate in top-twelve global wheat producers (1961–2019). (A) Worldwide wheat cropping area (%)29, total harvested area (106 hectares in 2019), and wheat production (megatons for 2019) of the top 12 global wheat producers (China, RUK—Russia, Ukraine, and Kazakhstan, India, USA—hard red winter (HRW) and hard red spring (HRS), France, Canada, Pakistan, Germany, Argentina, Turkey, Australia, and United Kingdom) (Map was generated in Python 3.8.5; http://www.python.org). (B) Changes in wheat yield (tons per hectare) and (C) climate—mean daily temperature (red dashed line; °C) and the seasonal water balance represented as potential evaporation minus precipitation (blue line; PET—P in millimeters of H2O). A positive trend in PET-P indicates an increase in water deficit. The seasonal atmospheric [CO2] in μmol CO2 per mol−1 air is also shown in the insert of C (black line). Temperature, PET-P, and [CO2] shown in C are averaged values over the wheat-growing period and the shared area of the wheat-growing areas of the top 12 global wheat producers. Decadal trends in temperature (red) and PET-P (blue) as well as the significance levels of these trends are presented in C.Full size imageWhile all these twelve major wheat producers saw an increase in yield during the last six decades (Fig. 1B), China displayed the most noteworthy increase with a nearly sevenfold higher yield in 2019 than in 1961 and a mean total increase of 5.19 tons ha−1 for the period of 1961–2019. Germany, the UK, and France reported comparable yield increases of 5.20 tons ha−1, 5.19 tons ha−1, and 4.81 tons ha−1, respectively, during this period, suggesting an approximately 1.6-fold improvement since 1961 (Fig. 1B). Australia, RUK, and Turkey reported the lowest gains with only 0.87 tons ha−1, 1.26 tons ha−1, and 1.71 tons ha−1, respectively, representing improvements of 67%, 150%, and 175% in yield per hectare since 1961.Yield increase occurred despite the steep rise in temperature (nearly 1.2 °C) in the twelve countries during the last six decades (Fig. 1C). Water deficit—calculated as the difference between potential evaporative demand and precipitation (PET—P; mm H2O y−1)—also increased by an average of (sim) 29 mm of H2O for the same period. Increases in yield since the early 1960s were likely due to breeding and agrotechnological advances, improved management, and a steep rise in atmospheric [CO2] of (sim) 98 μmol mol−1, from 315.9 μmol mol−1 in 1961 to 413.4 μmol mol−1 in 2019 (insert in Fig. 1C).Unraveling the impacts of climate and [CO2] on yieldBased on previous studies30,31, we used a log-linear model to quantify the impact of [CO2] and daily minimum (Tmin), maximum (Tmax), and mean (Tmean) temperatures, as well as seasonal water deficit (PET-P), and rainfall distribution on wheat yield. Climate variables were obtained from the TerraClimate data set32, while monthly records of [CO2] from the Mauna Loa station were used to model the effects of CO2 (see “Methods”). To quantify wheat yield as a function of climate variables and [CO2], we included all 12 countries in the regression analysis. Supplementary Table 2 presents summary statistics of all variables, while Supplementary Fig. 1 depicts trends in Tmean and PET-P per country.Since climate variables tend to be correlated over time (Supplementary Table 3), controlling for all of these variables in the model facilitates the estimation of their distinct effect on yield. We used country-specific trends to distinguish changes in wheat yield related to climate and [CO2] from those attributed to agrotechnological advancements, changes in country-specific policies, and other local-changing factors (e.g., economic and population growth; more information on how this was done can be found in “Methods”). We also included country-specific effects across all models to account for unobserved time-invariant heterogeneity at the country level, such as geographical properties, edaphic characteristics, and other local-specific features (see “Methods”).Table 1 reports the estimated regression coefficients of four models, (1) using only temperature variables (T), (2) temperature and water-related (i.e., seasonal rainfall distribution and water deficit as PET-P) variables (T + W), (3) including [CO2] (T + W + C), and (4) the interaction between [CO2] and climate variables (T + W + C + interactions).Table 1 Effects of climate variables and [CO2] on log wheat yields of the world’s major wheat producers.Full size tableAmong the temperature measures, only Tmean had a consistently significant effect on yield (p  More

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