- Field Crops Res.Grass modelling in data-limited areas by incorporating MODIS data productsXiao Huang, Gang Zhao, Conrad Zorn, Fulu Tao, Shaoqiang Ni, Wenyuan Zhang, Tongbi Tu, and Mats HöglindField Crops Research, 2021
Process-based grass models (PBGMs) are widely used for predicting grass growth under potential climate change and different management practices. However, accurate predictions using PBGMs heavily rely on field observations for data assimilation. In data-limited areas, performing robust and reliable estimates of grass growth remains a challenge. In this paper, we incorporated satellite-based MODIS data products, including leaf area index, gross primary production and evapotranspiration, as an additional supplement to field observations. Popular data assimilation methods, including Bayesian calibration and the updating method ensemble Kalman filter, were applied to assimilate satellite derived information into the BASic GRAssland model (BASGRA). A range of different combinations of data assimilating methods and data availability were tested across four grassland sites in Norway, Finland and Canada to assess the corresponding accuracy and make recommendations regarding suitable approaches to incorporate MODIS data. The results demonstrated that optimizing the model parameters that are specific for grass species and cultivar should be targeted prior to updating model state variables. The MODIS derived data products were capable of constraining model’s simulations on phenological development and biomass accumulation by parameter optimization with its performance exceeding model outputs driven by default parameters. By integrating even a small number of field measurements into the parameter calibration, the model’s predictive accuracy was further improved - especially at sites with obvious biases in the input MODIS data. Overall, this comparative study has provided flexible solutions with the potential to strengthen the capacity of PBGMs for grass growth estimation in practical applications.
- Curr. Biol.Habitat change and biased sampling influence estimation of diversity trendsWenyuan Zhang, Ben C. Sheldon, Richard Grenyer, and Kevin J. GastonCurrent Biology, 2021
Recent studies have drawn contrasting conclusions about the extent to which local-scale measures of biodiversity are declining, and whether such patterns conflict with the global-scale declines that have attracted much attention. A key source of high quality data for such analyses comes from longitudinal biodiversity studies which sample a given taxon repeatedly over time at a specific location. There has been relatively little consideration of how habitat change might lead to biases in the sampling and continuity of biodiversity time-series data, and the consequent potential for bias in the biodiversity trends that result. Here, based on analysis of standardised routes from the North American Breeding Bird Survey (3014 routes sampled over 18 years), we demonstrate that major local habitat change is associated with an increase in the rate of survey cessations. We further show that routes that were continued despite major habitat changes show reduced diversity. By simulating potential rates of loss, we show that the underlying real trends in taxonomic, functional and phylogenetic diversity can even reverse in sign if more than a quarter of diversity is lost from routes that ceased, and are thus no longer included in surveys. Our analyses imply that biodiversity loss can be underestimated by biases introduced if continued sampling in longitudinal studies is influenced by local change. We argue that researchers and conservation practitioners should be aware of the potential for bias in such data and seek to use more robust methods to evaluate biodiversity trends and make conservation decisions.
- Landsc. Ecol.Multiscale effects of habitat and surrounding matrices on waterbird diversity in the Yangtze River FloodplainBoyu Gao, Peng Gong, Wenyuan Zhang, Jun Yang, and Yali SiLandscape Ecology, 2021
With the expansion in urbanization, understanding how biodiversity responds to the altered landscape becomes a major concern. Most studies focus on habitat effects on biodiversity, yet much less attention has been paid to surrounding landscape matrices and their joint effects.We investigated how habitat and landscape matrices affect waterbird diversity across scales in the Yangtze River Floodplain, a typical area with high biodiversity and severe human-wildlife conflict. The compositional and structural features of the landscape were calculated at fine and coarse scales. The ordinary least squares regression model was adopted, following a test showing no significant spatial autocorrelation in the spatial lag and spatial error models, to estimate the relationship between landscape metrics and waterbird diversity. Well-connected grassland and shrub surrounded by isolated and regular-shaped developed area maintained higher waterbird diversity at fine scales. Regular-shaped developed area and cropland, irregular-shaped forest, and aggregated distribution of wetland and shrub positively affected waterbird diversity at coarse scales. Habitat and landscape matrices jointly affected waterbird diversity. Regular-shaped developed area facilitated higher waterbird diversity and showed the most pronounced effect at coarse scales. The conservation efforts should not only focus on habitat quality and capacity, but also habitat connectivity and complexity when formulating development plans. We suggest planners minimize the expansion of the developed area into critical habitats and leave buffers to maintain habitat connectivity and shape complexity to reduce the disturbance to birds. Our findings provide important insights and practical measures to protect biodiversity in human-dominated landscapes.
- Mov. Ecol.Reducing human pressure on farmland could rescue China’s declining wintering geeseYali Si, Jie Wei, Wenzhao Wu, Wenyuan Zhang, Lin Hou, Le Yu, and Ben WielstraMovement Ecology, 2020
Background. While goose populations worldwide benefit from food provided by farmland, China’s threatened wintering goose populations have failed to capitalize on farmland. It has been proposed that, due to an exceptionally intense human pressure on Chinese farmland, geese cannot exploit farmland in their wintering sites and hence are confined to their deteriorating natural habitat. If this were true, locally decreasing this human pressure on farmland ‘refuges’ would represent a promising conservation measure. Methods. We investigate habitat use of two declining migratory goose species in their core wintering (Yangtze River Floodplain) and stopover (Northeast China Plain) regions, compare the human pressure level at both regions, and adopt a mixed-effect resource selection function model to test how human pressure, food resource type (farmland or wetland/grass), distance to roosts, and their interaction terms influence the utilization of food resources for each species and region. To this aim we use satellite tracking of 28 tundra bean geese Anser serrirostris and 55 greater white-fronted geese A. albifrons, a newly produced 30 m land cover map, and the terrestrial human footprint map. Results. Geese use farmland intensively at their stopover site, but hardly at their wintering site, though both regions have farmland available at a similar proportion. The human pressure on both farmland and wetland/grass is significantly lower at the stopover region compared to the wintering region. At both sites, the two goose species actively select for farmland and/or wetland/grass with a relatively low human pressure, positioned relatively close to their roosting sites. Conclusions. Our findings suggest that if human pressure were to decrease in the farmlands close to the roost, China’s wintering geese could benefit from farmland. We recommend setting aside farmland near roosting sites that already experiences a relatively low human pressure as goose refuges, and adopt measures to further reduce human pressure and increase food quality and quantity, to help counter the decline of China’s wintering goose populations. Our study has important conservation implications and offers a practical measure for migratory waterfowl conservation in areas of high human-wildlife conflict.
- Environ. Model. Softw.GDNDC: An integrated system to model water-nitrogen-crop processes for agricultural management at regional scalesXiao Huang, Shaoqiang Ni, Chao Wu, Conrad Zorn, Wenyuan Zhang, and Chaoqing YuEnvironmental Modelling & Software, 2020
Agroecosystem modelling has increasingly focused on the integration of soil biogeochemical processes and crop growth. However, few models are available that offer high computing efficiencies for region-scale simulations, integrated decision support tools, and a structure that allows for easy extension. This paper introduces a new modelling tool to fill this gap: the GDNDC (Gridded DNDC) system for gridded agro-biogeochemical simulations. Based on the established DeNitrification and DeComposition (DNDC) model version-95, its main advancements include (i) implementation of parallel computation to significantly reduce computation time across multiple scales; (ii) a built-in parameter optimization algorithm to improve the predictive accuracy, and (iii) several decision support tools. We demonstrate each of these for county-level maize growth simulations in Liaoning Province (China) and reveal the potential of this new modelling tool to guide both long-term policy decisions regarding optimal fertilizer application and near-term crop yield forecasting for reactive decisions required in times of drought.
- Landsc. Ecol.Species-dependent effects of habitat degradation in relation to seasonal distribution of migratory waterfowl in the East Asian–Australasian FlywayYanjie Xu, Yali Si, Shenglai Yin, Wenyuan Zhang, Mikhail Grishchenko, Herbert HT Prins, Peng Gong, and Willem F BoerLandscape Ecology, 2019
Context. Migratory species’ resilience to landscape changes depends on spatial patterns of habitat degradation in relation to their migratory movements, such as the distance between breeding and non-breeding areas, and the location and width of migration corridors. Objectives. We investigated to what extent the impact of habitat degradation depended on the seasonal distributions of migratory waterfowl. Methods. Using logistic regression, we selected wetland sites for eight waterfowl species in the East Asian–Australasian Flyway (EAAF) by calculating the probabilities of species occurrence per wetland site in relation to environmental factors. We quantified landscape metrics related to habitat degradation within these wetland sites. We used general linear models to test for differences in the effects of habitat degradation on waterfowl species with different migration extents and at different latitudes. Results. The patterns of habitat degradation differed spatially across the EAAF and affected species to a different degree. Species with shorter and broader migration corridors (Anser cygnoid and A. anser) could benefit from improved habitat conditions in the west of the EAAF. Species with longer and narrower migration corridors (Cygnus columbianus, A. fabalis, A. albifrons, A. erythropus, Anas crecca, and Anas acuta) were under higher risk of habitat degradation in the coastal regions of China and Japan. Conclusions. Migratory species with longer and narrower migration corridors are more affected by habitat degradation, because they might have fewer alternative stopover sites at similar latitude. Our findings improve the understanding of species-specific effects of environmental changes on migratory species, and defines critical and endangered wetland sites, and vulnerable species.
- Comput. Electron. AgrA dynamic agricultural prediction system for large-scale drought assessment on the Sunway TaihuLight supercomputerXiao Huang, Chaoqing Yu, Jiarui Fang, Guorui Huang, Shaoqiang Ni, Jim Hall, Conrad Zorn, Xiaomeng Huang, and Wenyuan ZhangComputers and Electronics in Agriculture, 2018
Crop models are widely used to evaluate the response of crop growth to drought. However, over large geographic regions, the most advanced models are often restricted by available computing resource. This limits capacity to undertake uncertainty analysis and prohibits the use of models in real-time ensemble forecasting systems. This study addresses these concerns by presenting an integrated system for the dynamic prediction and assessment of agricultural yield using the top-ranked Sunway TaihuLight supercomputer platform. This system enables parallelization and acceleration for the existing AquaCrop, DNDC (DeNitrification and DeComposition) and SWAP (Soil Water Atmosphere Plant) models, thus facilitating multi-model ensemble and parameter optimization and subsequent drought risk analysis in multiple regions and at multiple scales. The high computing capability also opens up the possibility of real-time simulation during droughts, providing the basis for more effective drought management. Initial testing with varying core group numbers shows that computation time can be reduced by between 2.6 and 3.6 times. Based on the powerful computing capacity, a county-level model parameter optimization (2043 counties for 1996–2007) by Bayesian inference and multi-model ensemble using BMA (Bayesian Model Average) method were performed, demonstrating the enhancements in predictive accuracy that can be achieved. An application of this system is presented predicting the impacts of the drought of May–July 2017 on maize yield in North and Northeast China. The spatial variability in yield losses is presented demonstrating new capability to provide high resolution information with associated uncertainty estimates.
- Ecol. Evol.Spring migration patterns, habitat use, and stopover site protection status for two declining waterfowl species wintering in China as revealed by satellite trackingYali Si, Yanjie Xu, Fei Xu, Xueyan Li, Wenyuan Zhang, Ben Wielstra, Jie Wei, Guanhua Liu, Hao Luo, John Takekawa, Sivananintha Balachandran, Tao Zhang, Willem F Boer, Herbert HT Prins, and Peng GongEcology and Evolution, 2018
East Asian migratory waterfowl have greatly declined since the 1950s, especially the populations that winter in China. Conservation is severely hampered by the lack of primary information about migration patterns and stopover sites. This study utilizes satellite tracking techniques and advanced spatial analyses to investigate spring migration of the greater white‐fronted goose (Anser albifrons) and tundra bean goose (Anser serrirostris) wintering along the Yangtze River Floodplain. Based on 24 tracks obtained from 21 individuals during the spring of 2015 and 2016, we found that the Northeast China Plain is far‐out the most intensively used stopover site during migration, with geese staying for over 1 month. This region has also been intensely developed for agriculture, suggesting a causal link to the decline in East Asian waterfowl wintering in China. The protection of waterbodies used as roosting area, especially those surrounded by intensive foraging land, is critical for waterfowl survival. Over 90% of the core area used during spring migration is not protected. We suggest that future ground surveys should target these areas to confirm their relevance for migratory waterfowl at the population level, and core roosting area at critical spring‐staging sites should be integrated in the network of protected areas along the flyway. Moreover, the potential bird–human conflict in core stopover area needs to be further studied. Our study illustrates how satellite tracking combined with spatial analyses can provide crucial insights necessary to improve the conservation of declining Migratory species.
- Ecol. Indic.Multi-scale habitat selection by two declining East Asian waterfowl species at their core spring stopover areaWenyuan Zhang, Xinhai Li, Le Yu, and Yali SiEcological Indicators, 2018
Animals respond to their environment at multiple spatial scales that each require different conservation measures. Waterbirds are key bio-indicators for globally threatened wetland ecosystems but their multi-scale habitat selection mechanisms have rarely been studied. Using satellite tracking data and Maximum entropy modeling, we studied habitat selection of two declining waterfowl species, the Greater White-fronted Goose (Anser Albifrons) and the Tundra Bean Goose (A. serrirostris), at three spatial scales: landscape (30, 40, 50 km), foraging (10, 15, 20 km) and roosting (1, 3, 5 km). We hypothesized that the landscape-scale habitat selection was mainly based on relatively coarse landscape metrics, while more detailed landscape features were taken into account for the foraging- and roosting- scale habitat selection. We found that both waterfowl species preferred areas with a larger percentage of wetland and waterbodies at the landscape scale, aggregated waterbodies surrounded by scattered croplands at the foraging scale, and well-connected wetlands and well-connected middle-sized waterbodies at the roosting scale. The main difference in habitat selection for the two species occurred at the landscape and foraging scale; factors at the roosting scale were similar. We suggest that conservation activities should focus on enhancing the aggregation and connectivity of waterbodies and wetlands, and developing less aggregated cropland in the surroundings. Our approach could guide waterbird conservation practices and wetland management by providing effective measures to improve habitat quality in the face of human-induced environmental change.