State Key Laboratory of Earth Surface Processes and Resource Ecology

To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named 'Global Applications of Soil Erosion Modelling Tracker (GASEMT)', includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions.

Abstract Land use policies have turned southern China into one of the most intensively managed forest regions in the world, with actions maximizing forest cover on soils with marginal agricultural potential while concurrently increasing livelihoods and mitigating climate change. Based on satellite observations, here we show that diverse land use changes in southern China have increased standing aboveground carbon stocks by 0.11 ± 0.05 Pg C y −1 during 2002–2017. Most of this regional carbon sink was contributed by newly established forests (32%), while forests already existing contributed 24%. Forest growth in harvested forest areas contributed 16% and non-forest areas contributed 28% to the carbon sink, while timber harvest was tripled. Soil moisture declined significantly in 8% of the area. We demonstrate that land management in southern China has been removing an amount of carbon equivalent to 33% of regional fossil CO 2 emissions during the last 6 years, but forest growth saturation, land competition for food production and soil-water depletion challenge the longevity of this carbon sink service.

Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

Abstract. Crop phenology provides essential information for monitoring and modeling land surface phenology dynamics and crop management and production. Most previous studies mainly investigated crop phenology at the site scale; however, monitoring and modeling land surface phenology dynamics at a large scale need high-resolution spatially explicit information on crop phenology dynamics. In this study, we produced a 1 km grid crop phenological dataset for three main crops from 2000 to 2015 based on Global Land Surface Satellite (GLASS) leaf area index (LAI) products, called ChinaCropPhen1km. First, we compared three common smoothing methods and chose the most suitable one for different crops and regions. Then, we developed an optimal filter-based phenology detection (OFP) approach which combined both the inflection- and threshold-based methods and detected the key phenological stages of three staple crops at 1 km spatial resolution across China. Finally, we established a high-resolution gridded-phenology product for three staple crops in China during 2000–2015. Compared with the intensive phenological observations from the agricultural meteorological stations (AMSs) of the China Meteorological Administration (CMA), the dataset had high accuracy, with errors of the retrieved phenological date being less than 10 d, and represented the spatiotemporal patterns of the observed phenological dynamics at the site scale fairly well. The well-validated dataset can be applied for many purposes, including improving agricultural-system or earth-system modeling over a large area (DOI of the referenced dataset: https://doi.org/10.6084/m9.figshare.8313530; Luo et al., 2019).

Abstract Reliable and continuous information on major crop harvesting areas is fundamental to investigate land surface dynamics and make policies affecting agricultural production, land use, and sustainable development. However, there is currently no spatially explicit and time-continuous crop harvesting area information with a high resolution for China. The spatiotemporal patterns of major crop harvesting areas at a national scale have rarely been investigated. In this study, we proposed a new crop phenology-based crop mapping approach to generate a 1 km harvesting area dataset for three staple crops (i.e. rice, wheat, and maize) in China from 2000 to 2015 based on GLASS leaf area index (LAI) products. First, we retrieved key phenological dates of the three staple crops by combining the inflexion- and threshold-based methods. Then, we determined the grids cultivated for a certain crop if its three key phenological dates could be simultaneously identified. Finally, we developed crop classification maps and a dataset of annual harvesting areas (ChinaCropArea1 km), comprehensively considering the characteristics of crop phenology and the references of drylands and paddy fields. Compared with the county-level agricultural statistical data, the crop classification had a high accuracy, with R 2 values consistently greater than 0.8. The spatiotemporal patterns of major crop harvesting areas during the period were further analyzed. The results showed that paddy rice harvesting areas had expanded aggressively in northeastern China but decreased in southern China. Maize harvesting areas expanded substantially in major maize cultivation areas across China. Wheat harvesting areas declined overall, although they increased notably in their major production areas. The spatiotemporal patterns could be ascribed to various anthropogenic, biophysical, and social-economic drivers, including urbanization, reduced cropping intensity in southern China, frequent disasters from climate change, and large areas of abandoned farmland in northern and southwestern China. The resultant dataset can be applied for many purposes, including land surface modeling, agro-ecosystem modeling, agricultural production and land use policy-making.

Precise estimation of sediment transport capacity ( T c ) is critical to the development of physically based erosion models. Few data are available for estimating T c on steep slopes. The objectives of this study were to evaluate the effects of unit flow discharge ( q ), slope gradient ( S ), and mean flow velocity on T c in shallow flows and to investigate the relationship between T c and shear stress, stream power, and unit stream power on steep slopes using a 5‐m‐long and 0.4‐m‐wide nonerodible flume bed. Unit flow discharge ranged from 0.625 × 10 −3 to 5 × 10 −3 m 2 s −1 and slope gradient from 8.8 to 46.6%. The diameter of the test riverbed sediment varied from 20 to 2000 μm, with a median diameter of 280 μm. The results showed that T c increased as a power function with discharge and slope gradient with a coefficient of Nash–Sutcliffe model efficiency (NSE) of 0.95. The influences of S on T c increased as S increased, with T c being slightly more sensitive to q than to S The T c was well predicted by shear stress (NSE = 0.97) and stream power (NSE = 0.98) but less satisfactorily by unit stream power (NSE = 0.92) for the slope range of 8.8 to 46.6%. Mean flow velocity was also a good predictor of T c (NSE = 0.95). Mean flow velocity increased as q and S increased in this study. Overall, stream power seems to be the preferred predictor for estimating T c for steep slopes; however, the predictive relationships derived in this study need to be evaluated further in eroding beds using a range of soil materials under various slopes.

By using a land cover map, normalized difference vegetation index (NDVI) data sets, monthly meteorological data and observed net primary productivity (NPP) data, we have improved the method of estimating light use efficiency (LUE) for different biomes and soil moisture coefficients in the Carnegie–Ames–Stanford Approach (CASA) ecosystem model. Based on this improved model we produced an annual NPP map (in 1999) for the East Asia region located at 10–70° N, 70–170° E (about 19.66% of the terrestrial surface of the Earth). The results show that the mean NPP for the study area in 1999 was 374.12 g carbon (C) m−2 year−1 and the total NPP was 1.096 × 1014 kg C year−1, making up 17.51–18.39% of the global NPP. Comparison between the estimated NPP obtained from this improved CASA ecosystem model and the observed NPP obtained from two NPP databases indicates that the estimated NPP is close to the observed NPP, with an average error of 5.15% for the study region. We used two different land cover maps of China to drive the improved CASA model by keeping other inputs unchanged to determine how the classification accuracy of the land cover map affects the estimated NPP, and the results indicate that an accurate land cover map is important for obtaining an accurate and reliable estimate of NPP for some regions, especially for a particular biome.

Abstract. In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76 % of the experimental sites with agricultural land use as the dominant type (∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it.

Persian walnut (Juglans regia) is cultivated worldwide for its high-quality wood and nuts, but its origin has remained mysterious because in phylogenies it occupies an unresolved position between American black walnuts and Asian butternuts. Equally unclear is the origin of the only American butternut, J. cinerea. We resequenced the whole genome of 80 individuals from 19 of the 22 species of Juglans and assembled the genome of its relatives Pterocarya stenoptera and Platycarya strobilacea. Using phylogenetic-network analysis of single-copy nuclear genes, genome-wide site pattern probabilities, and Approximate Bayesian Computation, we discovered that J. regia (and its landrace J. sigillata) arose as a hybrid between the American and the Asian lineages and that J. cinerea resulted from massive introgression from an immigrating Asian butternut into the genome of an American black walnut. Approximate Bayesian Computation modeling placed the hybrid origin in the late Pliocene, ∼3.45 My, with both parental lineages since having gone extinct in Europe.

Maize is an extremely important grain crop, and the demand has increased sharply throughout the world. China contributes nearly one-fifth of the total production alone with its decreasing arable land. Timely and accurate prediction of maize yield in China is critical for ensuring global food security. Previous studies primarily used either visible or near-infrared (NIR) based vegetation indices (VIs), or climate data, or both to predict crop yield. However, other satellite data from different spectral bands have been underutilized, which contain unique information on crop growth and yield. In addition, although a joint application of multi-source data significantly improves crop yield prediction, the combinations of input variables that could achieve the best results have not been well investigated. Here we integrated optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield across four agro-ecological zones (AEZs) in China using a regression-based method (LASSO), two machine learning (ML) methods (RF and XGBoost), and deep learning (DL) network (LSTM). The results showed that combining multi-source data explained more than 75% of yield variation. Satellite data at the silking stage contributed more information than other variables, and solar-induced chlorophyll fluorescence (SIF) had an almost equivalent performance with the enhanced vegetation index (EVI) largely due to the low signal to noise ratio and coarse spatial resolution. The extremely high temperature and vapor pressure deficit during the reproductive period were the most important climate variables affecting maize production in China. Soil properties and management factors contained extra information on crop growth conditions that cannot be fully captured by satellite and climate data. We found that ML and DL approaches definitely outperformed regression-based methods, and ML had more computational efficiency and easier generalizations relative to DL. Our study is an important effort to combine multi-source remote sensed and environmental data for large-scale yield prediction. The proposed methodology provides a paradigm for other crop yield predictions and in other regions.

Wheat is a leading cereal grain throughout the world. Timely and reliable wheat yield prediction at a large scale is essential for the agricultural supply chain and global food security, especially in China as an important wheat producing and consuming country. The conventional approach using either climate or satellite data or both to build empirical and crop models has prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, socio-economic (SC) factors may also improve crop yield prediction, but their contributions need in-depth investigation, especially in regions with good irrigation conditions, sufficient fertilization, and pesticide application. Here, we performed the first attempt to predict wheat yield across China from 2001 to 2015 at the county-level by integrating multi-source data, including monthly climate data, satellite data (i.e., Vegetation indices (VIs)), and SC factors. The results show that incorporating all the datasets by using three machine learning methods (Ridge Regression (RR), Random Forest (RF), and Light Gradient Boosting (LightGBM)) can achieve the best performance in yield prediction (R2: 0.68~0.75), with the most individual contributions from climate (~0.53), followed by VIs (~0.45), and SC factors (~0.30). In addition, the combinations of VIs and climate data can capture inter-annual yield variability more effectively than other combinations (e.g., combinations of climate and SC, and combinations of VIs and SC), while combining SC with climate data can better capture spatial yield variability than others. Climate data can provide extra and unique information across the entire growing season, while the peak stage of VIs (Mar.~Apr.) do so. Furthermore, incorporating spatial information and soil proprieties into the benchmark models can improve wheat yield prediction by 0.06 and 0.12, respectively. The optimal wheat prediction can be achieved with approximately a two-month leading time before maturity. Our study develops timely and robust methods for winter wheat yield prediction at a large scale in China, which can be applied to other crops and regions.

The coastal part of China and its surrounding regions are dominated by a highly dense population and highly developed economy. Extreme precipitation events (EPEs) cause a lot of damage and hence changes in these events and their causes have been drawing considerable attention. This study investigated EPEs resulting from western North Pacific (WNP) tropical cyclones (TCs) and their potential link to El Niño–Southern Oscillation (ENSO), using TC track data, daily precipitation data from 2313 stations for 1951–2014, and the NCAR–NCEP reanalysis dataset. Two types of EPEs were considered: EPEs within 500 km from the TC center, and those caused by mesoscale and synoptic systems, referred to as predecessor rain events (PREs), beyond 1000 km from the TC center. Results indicated significant impacts of TCs on EPEs along the coastal areas, and discernable effects in inland areas of China. However, the effect of TCs on EPEs tended to be modulated by ENSO. During neutral years, inland areas of China are more affected by TC-induced extreme precipitation than during El Niño or La Niña years, with the highest density of TC tracks and larger-than-average numbers of tropical storms, typhoons, and landfalling TCs. During the El Niño phase, the central and eastern equatorial Pacific was characterized by higher sea surface temperature (SST), greater low-level vorticity (1000 hPa) and upper-level divergence (250 hPa), and stronger prevailing westerlies, which combined to trigger the movement of mean genesis to the eastern and southeastern WNP, resulting in fewer TCs passing through the Chinese territory.

East Asia has been hypothesized to be subdivided into two distinct northern and southern areas, separated by a band of dry climate that was far more severe in the early Tertiary but still exists today. However, this biogeographic hypothesis has rarely been tested using a molecular phylogeographic approach. We genotyped 70 populations throughout the distributional range of Asian butternuts (Juglans section Cardiocaryon) using eight chloroplast DNA regions, one single-copy nuclear gene, and 17 nuclear microsatellite loci, supplemented with paleodistribution modeling of the major genetic clades. The genetic data consistently identified two clades, one northern, comprising Juglans mandshurica and Juglans ailantifolia, and one southern, comprising Juglans cathayensis. The two clades diverged through climate-induced vicariance of an ancestral northern range during the mid-Miocene and remained mostly separate thereafter, with geographical isolation of the Japanese Islands and refugial isolation or secondary contacts in the late Pleistocene producing further subdivision within the northern clade. But beyond all that, we also discovered a role of environmental adaptation in maintaining and/or reinforcing the north-south divergence. Asian butternuts offer a strong case for the existence of a biogeographic divide between the northern and southern parts of East Asia during the Neogene and into the Pleistocene.

Abstract. A new temperature goal of “holding the increase in global average temperature well below 2 ∘C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 ∘C above pre-industrial levels” has been established in the Paris Agreement, which calls for an understanding of climate risk under 1.5 and 2.0 ∘C warming scenarios. Here, we evaluated the effects of climate change on growth and productivity of three major crops (i.e. maize, wheat, rice) in China during 2106–2115 in warming scenarios of 1.5 and 2.0 ∘C using a method of ensemble simulation with well-validated Model to capture the Crop–Weather relationship over a Large Area (MCWLA) family crop models, their 10 sets of optimal crop model parameters and 70 climate projections from four global climate models. We presented the spatial patterns of changes in crop growth duration, crop yield, impacts of heat and drought stress, as well as crop yield variability and the probability of crop yield decrease. Results showed that climate change would have major negative impacts on crop production, particularly for wheat in north China, rice in south China and maize across the major cultivation areas, due to a decrease in crop growth duration and an increase in extreme events. By contrast, with moderate increases in temperature, solar radiation, precipitation and atmospheric CO2 concentration, agricultural climate resources such as light and thermal resources could be ameliorated, which would enhance canopy photosynthesis and consequently biomass accumulations and yields. The moderate climate change would slightly worsen the maize growth environment but would result in a much more appropriate growth environment for wheat and rice. As a result, wheat, rice and maize yields would change by +3.9 (+8.6), +4.1 (+9.4) and +0.2 % (−1.7 %), respectively, in a warming scenario of 1.5 ∘C (2.0 ∘C). In general, the warming scenarios would bring more opportunities than risks for crop development and food security in China. Moreover, although the variability of crop yield would increase from 1.5 ∘C warming to 2.0 ∘C warming, the probability of a crop yield decrease would decrease. Our findings highlight that the 2.0 ∘C warming scenario would be more suitable for crop production in China, but more attention should be paid to the expected increase in extreme event impacts.

Wetlands are an important transitional ecosystem, and they play an important role in maintaining ecological balance. However, human activities and climate change have led to a decrease in wetlands. Therefore, to explore the degree of damage and assess the future trends of Guangxi wetlands, this study used the Google Earth Engine (GEE) cloud platform, and the support vector machine algorithm was selected for comparison and analysis to simulate the accuracy of land cover. GIS was used to analyse the evolution and degree of damage of nearly 30 Guangxi wetlands. The geographic detector model was used to explore the driving mechanism, and finally, the CA-Markov model and multi-scenario simulation were used to predict the wetland evolution from 2018 to 2035 to reveal the future direction of development. The results showed that (1) From 1990 to 2018, paddy fields accounted for the largest proportion of wetlands in Guangxi. In the past 30 years, the total area of wetlands in Guangxi has been degraded, with a total decrease of 983.33 km2. (2) The degree of wetland damage results showed that the total damaged area was greater than the total restored area, The wetland damage in Nanning city was the most serious, with an area difference of 503.22 km2 between the damaged and restored areas. (3) The analysis of the driving mechanism of wetland damage showed that distance from cities and towns, average precipitation and population density were the main driving factors. (4) The spatial distribution of natural development and economic construction in 2035 will be slightly damaged; additionally, the spatial distribution of ecological protection will expand as a whole. From 2025 to 2035, wetlands will be basically stable under Natural Development Scenario (NDS), degraded each year under the Economic Construction Scenario (ECS), and steadily increased each year under Ecological Protection Scenario (EPS).

Summary Whether species demography and diversification are driven primarily by extrinsic environmental changes such as climatic oscillations in the Quaternary or by intrinsic biological interactions like coevolution between antagonists is a matter of active debate. In fact, their relative importance can be assessed by tracking past population fluctuations over considerable time periods. We applied the pairwise sequentially Markovian coalescent approach on the genomes of 11 temperate Juglans species to estimate trajectories of changes in effective population size ( N e ) and used a Bayesian‐coalescent based approach that simultaneously considers multiple genomes ( G‐PhoCS ) to estimate divergence times between lineages. N e curves of all study species converged 1.0 million yr ago, probably reflecting the time when the walnut genus last shared a common ancestor. This estimate was confirmed by the G‐Pho CS estimates of divergence times. But all species did not react similarly to the dramatic climatic oscillations following early Pleistocene cooling, so the timing and amplitude of changes in N e differed among species and even among conspecific lineages. The population histories of temperate walnut species were not driven by extrinsic environmental changes alone, and a key role was probably played by species‐specific factors such as coevolutionary interactions with specialized pathogens.

Summary Anthropogenic conversion of natural wetlands into artificial wetland habitats has produced complex wetland landscapes worldwide. In this study we investigated the responses of migratory and wintering waterbirds to five artificial wetland habitats (aquaculture ponds, paddyfields, irrigation canals, open water reservoirs and saltpans) within a novel natural-artificial wetland landscape, Yellow River Delta (YRD), eastern China from October 2007 to May 2008. The results showed that almost all bird community indicators in the YRD natural wetlands were higher than those in adjacent artificial wetlands. Across the landscape, natural wetlands remained most important for all waterbird guilds, and more than 90% of waterbird populations were dependent on these habitats. Artificial wetlands mainly provided a secondary role, supporting about 70% of waterbird species (including six species that reached 1% of their global or biogeographical flyway populations), but with distinctive functional capacity for specific waterbird guilds in different artificial wetlands. The conservation value of artificial wetlands is often ephemeral, mainly during autumn, for specific migratory waterbirds and complements that of remaining areas of natural wetlands. Therefore, the utilisation patterns of artificial wetlands are highly temporal and the majority of species are dependent on areas of natural wetland. A comprehensive study of the inter-seasonal and inter-annual variations in these different habitats and dependence by the various guilds in the YRD is required to enable the true value of these habitats to be understood. We suggest that the conservation of artificial wetlands should not be at the expense of natural wetlands, which should remain the priority for wetland landscape management. Management to maintain the existing artificial wetlands for migrating and wintering water birds should target habitat features that are absent or limited in natural wetlands thus increasing the carrying capacity of the YRD landscape.

Urbanization is a human-dominated process and has greatly impacted biodiversity, ecosystem processes, and regional climate. In this study we assess the effect of different degrees of urbanization on land surface temperature using remote sensing images. Landsat TM images were used for land surface temperature retrieval using the algorithm proposed by Artis and Carnahan. ALOS multispectral images were used for landcover classification using classification trees in three study areas, namely Xicheng district(A), Haidian district(B), Shijingshan district(C), of different degrees of urbanization in Beijing. Landcover-specific surface temperatures were estimated through an inversion alorithm. At the different degrees of urbanization, reducing the within-pixel coverage ratio of vegetations will result in an land surface temperature rise. Quantitative assessment of the relationship between different degrees of urbanization and land surface temperature was simulated by an urbanization index which integrates the coverage ratio of built-up landcover type and the cell-average NDVI. Urbanization indices of the Xicheng district, Haidian district, Shijingshan district were calculated to be 0.91, 0.72, and, 0.55 respectively. Such results are consistent with the trend of evaluation using quantitative estimation land surface temperature.

Carbon allocation is one of the most important physiological processes to optimize the plant growth, which exerts a strong influence on ecosystem structure and function, with potentially large implications for the global carbon budget. However, it remains unclear how the carbon allocation pattern has changed at global scale and impacted terrestrial carbon uptake. Based on the Community Atmosphere Biosphere Land Exchange (CABLE) model, this study shows the increasing partitioning ratios to leaf and wood and reducing ratio to root globally from 1979 to 2014. The results imply the plant optimizes carbon allocation and reaches its maximum growth by allocating more newly acquired photosynthate to leaves and wood tissues. Thus, terrestrial vegetation has absorbed 16% more carbon averagely between 1979 and 2014 through adjusting their carbon allocation process. Compared with the fixed carbon allocation simulation, the trend of terrestrial carbon sink from 1979 to 2014 increased by 34% in the adaptive carbon allocation simulation. Our study highlights carbon allocation, associated with climate change, needs to be mapped and incorporated into terrestrial carbon cycle estimates.

Abstract Political borders and natural boundaries of wildlife populations seldom coincide, often to the detriment of conservation objectives. Transnational monitoring of endangered carnivores is rare, but is necessary for accurate population monitoring and coordinated conservation policies. We investigate the benefits of collaboratively monitoring the abundance and survival of the critically endangered Amur leopard, which occurs as a single transboundary population across China and Russia. Country‐specific results overestimated abundance and were generally less precise compared to integrated monitoring estimates; the global population was similar in both years: 84 (70–108, 95% confidence interval). Uncertainty in country‐specific annual survival estimates were approximately twice the integrated estimates of 0.82 (0.69–0.91, 95% confidence limits). This collaborative effort provided a better understanding of Amur leopard population dynamics, represented a first step in building trust, and lead to cooperative agreements to coordinate conservation policies.