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Testing ecological models involves using independent data on model performance, which can be difficult or practically impossible to obtain. Individual based models of forest dynamics, or gap models, simulate the change of forests by computing the annual growth, birth and death of each tree at a location in a forest. The models are relatively simple and simulate results that can be translated to multiple response scales: Individual plant growth, population birth-death processes, stand environmental dynamics ( e.g ., evapotranspiration, element cycling, heat flux, etc .), landscape processes, and regional and global change. This paper reviews some of the approaches applied to testing gap models. Then, it demonstrates the testing of the performance of an individual-based gap model of forest dynamics, FAREAST, through comparison against independent data from China and across Russia. As part of this model testing, biomass simulation output for 93 locations is compared to independent field-collected inventory-data from 44 Russian forests, which span a broad range of forest types across Russia. FAREAST captures biomass dynamics and stabilization at specific locations bracketing the measured values. At Changbai Mountain, the model accurately predicts the community dynamics of complex mixed forest types present along an elevational gradient, as well as the broad regional compositional patterns across China and Russia. Validation of regional detailed landscape dynamics shows the model performs with fidelity with an average R2 value of 0.74 for 87 comparisons and an average root mean square error of 10.8 tC ha −1. Performance of the model for historical conditions implies the model's applicability across a broad region and suggests the usefulness of a detailed model for evaluating forest change to management and changing climate. [ABSTRACT FROM AUTHOR]

Runoff and evapotranspiration are two key variables of water budget in forest ecosystems. Modeling runoff and evapotranspiration dynamics play a vital role in assessing the hydrology cycle and function of forest ecosystems. Based on the hydrological and meteorological data collected over 20 years from January of 1988 to December of 2007 at Huitong National Forest Ecosystem Research Station, we used back propagation neural network (BPNN) and genetic neural network (GNN) models to simulate runoff and evapotranspiration of Chinese fir plantations for two watersheds located in Huitong county of Hunan Province, China. The purpose of this study was to accurately simulate runoff and evapotranspiration dynamics using both BPNN and GNN models. The model simulations of the runoff and evapotranspiration indicated that the GNN model concurrently possesses efficiency, effectiveness, and robustness. Moreover, the simulated results of GNN and BPNN model were compared with a multivariate statistics (M-slat) model. We found that the GNN model performed better than M-slat and BPNN models for modeling both runoff and evapotranspiration of Chinese fir plantations in China. [Copyright &y& Elsevier]

The implementation of systemic modeling methods to understand the variation of natural capital and ecosystem services in response to economic activities has huge significance for sound decision-making also at local scale. This paper reports the results of an investigation performed on the grassland ecosystem in North Xinjiang. The Emergy Synthesis method introduced by H.T. Odum in the 1980s was used to dynamically simulate the trend of grassland natural capital and its related ecosystem services. The simulation of natural capital was based on indexes of standing biomass, soil organic matter, soil nitrogen and soil water storages; ecosystem services were assessed by means of indexes of annual CO2 uptake, O2 release, forage supply as animal feed, food supply for human nutrition, and finally soil conservation. Results indicate that an upper limit of livestock carrying capacity in the region was reached in the year 1994; after that year the natural capital and main ecosystem services of the grassland ecosystem declined steadily. The emergy based currency equivalent value of the grassland ecosystem in terms of ecosystem services was higher than 87.3 billion Yuan RMB/yr in 1990. Such a value declined to 62.3 billion Yuan RMB/yr in 2010. Similarly, the total emergy based value of natural capital was about 331.7 billion Yuan RMB in 1990 and dropped to 155.6 billion Yuan RMB in 2010. According to the simulation model, the natural capital components such as biomass and soil stocks, the ecosystem services and the replacement value declined due to intensive herding and disturbance from human activities. Such trend is not sustainable because it exceeds the renewable carrying capacity of the area, but it is likely to continue until fundamental changes in human behavior and management of the grassland and animal husbandry occur or until the whole area is fully degraded. Social reasons prevent from simply decreasing the intensity of the livestock activities that provide living means to a large number of local farmers. As a consequence, the traditional low-productivity and environmental unfriendly grazing farming system should gradually be converted into a human-managed pasture based on farming higher productivity pasture crops in a small part of the local land, in order to decrease the grazing pressure on natural grassland of North Xinjiang. [Copyright &y& Elsevier]

Agriculture has been identified as the major contributor of non-point source pollution of Hainan water resources. In this study, we coupled the Xinanjiang model and SWAT to a new model, EcoHAT, to assess the non-point source pollution in Hainan. The study site is located around the Songtao reservoir, Hainan Island, China, which is regarding as the most important water source system for Hainan. EcoHAT, including algorithms for the hydrological cycle, nutrient cycle, and plant growth cycle, simulated the non-point source pollution for the watershed in calculated grid cell units based on remote sensing data. Remote sensing data were used to interpret the spatial land surface information and derive the model parameters. Besides the remote sensing data, other essential databases such as the meteorological databases, soil chemical and physical databases, and plant nutrients databases were also used in this study. The EcoHAT model was calibrated and validated with 5 years of monitored water quantity and quality data in the Songtao reservoir watershed. The study results indicated that the EcoHAT model has simulated the hydrologic pollutant adequately. After the calibration and validation, the parameters were applied to simulate the nutrient and sediment transport in the Songtao watershed during 2003–2007. In the end, the effects of several specific scenarios of changes in the land covers or management practices on the local watershed nutrients transport were also simulated. The results revealed that: (1) the model has predicted the runoff volume within a range of acceptable accuracy which was reflected by a large coefficient of determination; (2) regression analysis between the observed and simulated values resulted in high values of coefficient of determination (R 2) during the calibration and validation period. The high values of Nash–Sutcliffe simulation efficiency were achieved with a close agreement between the observed and simulated pollutants concentrations in the runoff. It also indicated that the model simulated the NO3–N, NH4 +–N, and P concentrations in the runoff for the Songtao watershed with considerable accuracy; (3) the sediment loads, TN, and TP, experienced temporal and spatial variations, with strong correlations existing between the parameters and the land use as well as the precipitation; (4) the scenario analysis showed that with 40% fertilization reduction, 7.51% and 7.76% reduction on TN and TP loads respectively could be reached. Besides, the conservation measures are more effective in the study area to reduce the sediment loads than in other areas. [Copyright &y& Elsevier]

The Amur tiger (Panthera tigris altaica) is a flagship species of the boreal forest ecosystem in northeastern China and Russia Far East. During the past century, the tiger population has declined sharply from more than 3000 to fewer than 600 individuals, and its habitat has become much smaller and greatly fragmented. Poaching, habitat degradation, habitat loss, and habitat fragmentation have been widely recognized as the primary causes for the observed population decline. Using a population viability analysis tool (RAMAS/GIS), we simulated the effects of poaching, habitat degradation, habitat loss, and habitat fragmentation on the population dynamics and extinction risk of the Amur tiger, and then explored the relative effectiveness of three conservation strategies involving improving habitat quality and establishing movement corridors in China and Russia. A series of controlled simulation experiments were performed based on the current spatial distribution of habitat and field-observed vital rates. Our results showed that the Amur tiger population could be viable for the next 100 years if the current habitat area and quality were well-maintained, with poaching strictly prohibited of the tigers and their main prey species. Poaching and habitat degradation (mainly prey scarcity) had the largest negative impacts on the tiger population persistence. While the effect of habitat loss was also substantial, habitat fragmentation per se had less influence on the long-term fate of the tiger population. However, to sustain the subpopulations in both Russia and China would take much greater conservation efforts. The viability of the Chinese population of tigers would rely heavily on its connectivity with the largest patch on the other side of the border. Improving the habitat quality of small patches only or increasing habitat connectivity through movement corridors alone would not be enough to guarantee the long-term population persistence of the Amur tiger in both Russia and China. The only conservation strategy that allowed for long-term persistence of tigers in both countries required both the improvement of habitat quality and the establishment of a transnational reserve network. Our study provides new insights into the metapopulation dynamics and persistence of the Amur tiger, which should be useful in landscape and conservation planning for protecting the biggest cat species in the world. [Copyright &y& Elsevier]

A process-based crop growth model (Vegetation Interface Processes (VIP) model) is used to estimate crop yield with remote sensing over the North China Plain. Spatial pattern of the key parameter—maximum catalytic capacity of Rubisco (V cmax) for assimilation is retrieved from Normalized Difference of Vegetation Index (NDVI) from Terra-MODIS and statistical yield records. The regional simulation shows that the agreements between the simulated winter wheat yields and census data at county-level are quite well with R 2 being 0.41–0.50 during 2001–2005. Spatial variability of photosynthetic capacity and yield in irrigated regions depend greatly on nitrogen input. Due to the heavy soil salinity, the photosynthetic capacity and yield in coastal region is less than 50μmolCm−2 s−1 and 3000kgha−1, respectively, which are much lower than that in non-salinized region, 84.5μmolCm−2 s−1 and 5700kgha−1. The predicted yield for irrigated wheat ranges from 4000 to 7800kgha−1, which is significantly larger than that of rainfed, 1500–3000kgha−1. According to the path coefficient analysis, nitrogen significantly affects yield, by which water exerts noticeably indirect influences on yield. The effect of water on yield is regulated, to a certain extent, by crop photosynthetic capacity and nitrogen application. It is believed that photosynthetic parameters retrieved from remote sensing are reliable for regional production prediction with a process-based model. [Copyright &y& Elsevier]

Water use efficiency (WUE) is an important variable used in climate change and hydrological studies in relation to how it links ecosystem carbon cycles and hydrological cycles together. However, obtaining reliable WUE results based on site-level flux data remains a great challenge when scaling up to larger regional zones. Biophysical, process-based ecosystem models are powerful tools to study WUE at large spatial and temporal scales. The Integrated BIosphere Simulator (IBIS) was used to evaluate the effects of climate change and elevated CO2 concentrations on ecosystem-level WUE (defined as the ratio of gross primary production (GPP) to evapotranspiration (ET)) in relation to terrestrial ecosystems in China for 2009–2099. Climate scenario data (IPCC SRES A2 and SRES B1) generated from the Third Generation Coupled Global Climate Model (CGCM3) was used in the simulations. Seven simulations were implemented according to the assemblage of different elevated CO2 concentrations scenarios and different climate change scenarios. Analysis suggests that (1) further elevated CO2 concentrations will significantly enhance the WUE over China by the end of the twenty-first century, especially in forest areas; (2) effects of climate change on WUE will vary for different geographical regions in China with negative effects occurring primarily in southern regions and positive effects occurring primarily in high latitude and altitude regions (Tibetan Plateau); (3) WUE will maintain the current levels for 2009–2099 under the constant climate scenario (i.e. using mean climate condition of 1951–2006 and CO2 concentrations of the 2008 level); and (4) WUE will decrease with the increase of water resource restriction (expressed as evaporation ratio) among different ecosystems. [Copyright &y& Elsevier]

The natural grassland ecosystem of the Xilingol steppe has traditionally been the source of the most productive and highest quality agriculture in northern China. Unfortunately, the area is now experiencing degradation due to resource overuse. In an attempt to forecast grassland production and to sustain the ecosystem, we built a time-dependent simulation model of the ecosystem based on long-range weather forecasts (several weeks to several months). The model incorporated five state variables including above- and belowground biomass, the amount of standing dead plant material, livestock (sheep) weight, and the amount of excrement per unit ground area. Within the model, solar light energy is fixed by grassland vegetation and flows through the other variables via a variety of organism–environment interactions. The model was written using a set of simultaneous differential equations and was numerically analyzed. The values of the time-dependent parameters controlling energy flow were determined based on data accumulated in experiments and field surveys executed at a grassland experimental station located in Xilingol, as well as by reference to related literature. We used daily meteorological data including air temperature and rainfall recorded at the Xilingol Meteorological Observatory. Simulated results for several stocking densities coincided well with the data of aboveground plant biomass observed at the experimental station in 1990, 1993, and 1997. We obtained reasonable simulation results for five stocking densities, three air temperature patterns, and five rainfall patterns. When a month-long drought, which sometimes occurs in this area, was forecast by a local weather station, a decrease in grassland production was forecast by the model. Such forecasts will assist in the management of livestock, forage preservation, and grassland conservation. [Copyright &y& Elsevier]

Solar radiation is an important parameter in ecological process modeling, hydrological modeling and bio-physical modeling. However, models focusing on solar radiation in relation to giant panda habitat and seasonal distribution are limited. The research aims to form spatial models of 12 month solar radiation patterns and to investigate the relation between the solar radiation patterns and the monthly distribution patterns of giant pandas. The solar radiation model of Kumar et al. was adopted for this study in Foping Nature Reserve (NR), China. By comparing twelve monthly solar radiation patterns and calculating statistics such as maximum, minimum, mean and standard deviation of the solar radiation, diversified solar radiation patterns over different months were obtained. Maximum solar radiation occurred in June and July, while minimum solar radiation occurred in December and January. The annual sum of solar radiation was 6954MJ/m2 in Foping NR. The range in solar radiation was smaller in hot months and larger in cold months. Radio tracking data of giant pandas were collected for twelve months and the ensuing maps were overlaid with the twelve-month solar radiation map to analyze the relation between the giant panda's monthly distribution and solar radiation. Our results showed that giant pandas prefer areas with lower solar radiation in warm months and select areas with higher solar radiation in cold months, which illustrates that the distribution of giant pandas is indeed affected by solar radiation. To a certain degree, it also explains the behavior of seasonal movement by giant pandas in Foping NR. [Copyright &y& Elsevier]

Arrhenius is mainly given credit for his discovery of the log-log relationship between number of species and area, but in 1921 he invented the technique of analysing the data with a null model. This achievement, which Arrhenius explicitly stated was more important than the empirical species–area relationship, has been overlooked in the scientific literature. We suggest here that analysis of large communities should be done by a successive addition of predefined subsets of samples. Plotting the resulting accumulation curves in a common diagram usually will reveal heterogeneity between subsamples. We believe that this is a general feature of a counting process, (over space or time), that keeps record of new objects being generated by an unobservable process. We show this by two nonbiological time series: examination scores at the University of Oslo and stock prices on the Oslo stock market. We then apply these ideas to the analysis of the nested accumulation plots obtained from two unusually large community samples, the benthos of the Norwegian continental shelf and of marine areas of Hong Kong. Discontinuities in the accumulation curves can be explained by changes in habitat heterogeneity. These changes may be interpreted as variations in the parameter values in the class of null models discovered by Arrhenius. By selecting appropriate parameter values, the observed accumulation curves (calculated by an analytical expression giving exact values) can be approximated by null curves. This procedure makes it possible to identifying the major factors responsible for the shape of the curves in the accumulation plots. [ABSTRACT FROM AUTHOR]