Nitrogen and Phosphorus Cycling in Shrimp Ponds and the Measures for Sustainable Management

Six ponds of age 3 were selected 45 km north from Suzhou in the Tailake region, and research conducted on nitrogen and phosphorus cycling in P. vannanmei(Penaeus vannanme) ponds and M. nipponense(Macrobrachium nipponense) hatchery ponds under normal management. Two treatments each had three replications. The results confirmed that feed was the major path of nitrogen and phosphorus input, each accounted for 61.24%(193.81 kg ha^–1) and 81.08%(45.20 kg ha^–1) of the total nitrogen and phosphorus input for P. vannanme ponds; the values for M. nipponense ponds were 43.93%(86.31 kg ha^–1) and 57.67%(14.61 kg ha^–1), respectively. Water pumped into ponds contributed on average 83.57 kg ha^–1 nitrogen and 8.48 kg ha^–1 phosphorus for P. vannanmei ponds, and 87.48 kg ha^–1 nitrogen and 7.00 kg ha^–1 phosphorus for M. nipponense hatchery ponds. Shrimp harvest recovered 102.81 kg ha^–1 nitrogen (32.94% of the total nitrogen input) and 7.94 kg ha^–1phosphorus (14.23% of the total phosphorus input) for P. vannanme ponds; and 43.94 kg ha^–1 nitrogen and 4.46 kg ha^–1phosphorus for M. nipponense hatchery ponds. The sum of nitrogen losses through volatilization, denitrification and sedimentation was 173.62 and 122.39 kg ha^–1, 54.86% and 62.29% of the total nitrogen input for P. vannanme ponds and M. nipponense hatchery ponds, respectively. Sediment accumulated 41.46 and 14.63 kg ha^–1 phosphorus, 74.37% and 64.85% of the total phosphorus input for P. vannanm ponds and M. nipponense hatchery ponds. Draining and seeping caused 40.06 kg ha^–1 nitrogen (12.66% of total nitrogen input) and 6.36 kg ha^–1 phosphorus (11.40% of total phosphorus input) loss to the surrounding water from P. vannanme ponds in 114 days; 30.14 kg ha^–1nitrogen (15.34% of the total input) and 4.45 kg ha^–1 phosphorus (17.57% of the total input) to channel water from M. nipponense hatchery ponds in 87 days, respectively. Countermeasures for sustainable pond management include improving feeds and feeding, sediment treatments, machine aerating, chemicals with no pollution, and integrated fish-shrimp cultivation. Management of water resources for pond and methods to reduce nitrogen and phosphorus loading into surrounding water from drainage are elucidated.     

Review and comparison of methods to study the contribution of variables in artificial neural network models

Convinced by the predictive quality of artificial neural network (ANN) models in ecology, we have turned our interests to their explanatory capacities. Seven methods which can give the relative contribution and/or the contribution profile of the input factors were compared: (i) the ‘PaD’ (for Partial Derivatives) method consists in a calculation of the partial derivatives of the output according to the input variables; (ii) the ‘Weights’ method is a computation using the connection weights; (iii) the ‘Perturb’ method corresponds to a perturbation of the input variables; (iv) the ‘Profile’ method is a successive variation of one input variable while the others are kept constant at a fixed value; (v) the ‘classical stepwise’ method is an observation of the change in the error value when an adding (forward) or an elimination (backward) step of the input variables is operated; (vi) ‘Improved stepwise a’ uses the same principle as the classical stepwise, but the elimination of the input occurs when the network is trained, the connection weights corresponding to the input variable studied is also eliminated; (vii) ‘Improved stepwise b’ involves the network being trained and fixed step by step, one input variable at its mean value to note the consequences on the error. The data tested in this study concerns the prediction of the density of brown trout spawning redds using habitat characteristics. The PaD method was found to be the most useful as it gave the most complete results, followed by the Profile method that gave the contribution profile of the input variables. The Perturb method allowed a good classification of the input parameters as well as the Weights method that has been simplified but these two methods lack stability. Next came the two improved stepwise methods (a and b) that both gave exactly the same result but the contributions were not sufficiently expressed. Finally, the classical stepwise methods gave the poorest results.     

Influence of human-induced disturbance on benthic microbial metabolism in the Pichavaram mangroves,Vellar–Coleroon estuarine complex,India

Large areas of mangroves in India are heavily disturbed by cattle grazing, hypersalinity, and other human-induced impacts. In two disturbed Avicennia marina forests and two undisturbed A. marina and Rhizophora apiculata forests in the Pichavaram mangroves of the Vellar–Coleroon estuarine complex, southeast India, we measured the rates and pathways of microbial decomposition of soil organic matter to determine if human impact is altering biogeochemical activity within these stands. Rates of total carbon oxidation (T_COX) were higher in the undisturbed A. marina forest (mean 199 mol C m^–2 year^–1) than in the two impacted stands (43 and 79 mol C m^–2 year^–1); rates of total carbon oxidation in the R. apiculata forest averaged 75 mol C m^–2 year^–1. Sulphate reduction (range 21–319 mmol S m^–2 day^–1) was the major decomposition pathway (65–85% of T_COX), except at the most disturbed forest (30% of T_COX). Rates of sulphate reduction at all sites peaked in sub-surface soils to a depth of about 1 m, leading to little carbon burial (3–5% of total C input). There was some evidence of measurable iron and manganese reduction in association with tree roots. Rates of microbial activity were rapid in comparison with rates measured in other mangrove soils, reflecting high rates of phytoplankton production and organic matter retention in this lagoon. Human-induced disturbance creates a sharp zonation of dry, hypersaline soil overlying less saline, wetter soil, suppressing surface microbial and root growth. We conclude that this vertical alteration of soil characteristics and biogeochemistry shifts the cycling of nutrients between trees and microbes to a disequilibrium state, partly explaining why mangroves are stunted in these declining forests.Communicated by G. F. Humphrey, Sydney     

Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests

Forestry science has a long tradition of studying the relationship between stand productivity and abiotic and biotic site characteristics, such as climate, topography, soil and vegetation. Many of the early site quality modelling studies related site index to environmental variables using basic statistical methods such as linear regression. Because most ecological variables show a typical non-linear course and a non-constant variance distribution, a large fraction of the variation remained unexplained by these linear models. More recently, the development of more advanced non-parametric and machine learning methods provided opportunities to overcome these limitations. Nevertheless, these methods also have drawbacks. Due to their increasing complexity they are not only more difficult to implement and interpret, but also more vulnerable to overfitting. Especially in a context of regionalisation, this may prove to be problematic. Although many non-parametric and machine learning methods are increasingly used in applications related to forest site quality assessment, their predictive performance has only been assessed for a limited number of methods and ecosystems.In this study, five different modelling techniques are compared and evaluated, i.e. multiple linear regression (MLR), classification and regression trees (CART), boosted regression trees (BRT), generalized additive models (GAM), and artificial neural networks (ANN). Each method is used to model site index of homogeneous stands of three important tree species of the Taurus Mountains (Turkey): Pinus brutia, Pinus nigra and Cedrus libani. Site index is related to soil, vegetation and topographical variables, which are available for 167 sample plots covering all important environmental gradients in the research area. The five techniques are compared in a multi-criteria decision analysis in which different model performance measures, ecological interpretability and user-friendliness are considered as criteria.When combining these criteria, in most cases GAM is found to outperform all other techniques for modelling site index for the three species. BRT is a good alternative in case the ecological interpretability of the technique is of higher importance. When user-friendliness is more important MLR and CART are the preferred alternatives. Despite its good predictive performance, ANN is penalized for its complex, non-transparent models and big training effort.     

Modelling the productivity of naturalised pasture in the North Island,New Zealand: a decision tree approach

Decision tree, one of the data mining methods, has been widely used as a modelling approach and has shown better predictive ability than traditional approaches (e.g. regression). However, very little is known from the literature about how the decision tree performs in predicting pasture productivity. In this study, decision tree models were developed to investigate and predict the annual and seasonal productivity of naturalised hill-pasture in the North Island, New Zealand, and were compared with regression models with respect to model fit, validation and predictive accuracy. The results indicated that the decision tree models for annual and seasonal pasture productivity all had a smaller average squared error (ASE) and a higher percentage of correctly predicted cases than the corresponding regression models. The decision tree model for annual pasture productivity had an ASE which was only half of that of the regression model, and correctly predicted 90% of the cases in the model validation which was 10.8 percentage points higher than that of the regression model. Furthermore, the decision tree models for annual and seasonal pasture productivity also clearly revealed the relative importance of environmental and management variables in influencing pasture productivity, and the interaction among these variables. Spring rainfall was the most significant factor influencing annual pasture productivity, while hill slope was the most significant factor influencing spring and winter pasture productivity, and annual P fertiliser input and autumn rainfall were the most significant factors influencing summer and autumn pasture productivity. One limitation of using the decision tree to predict pasture productivity was that it did not generate a continuous prediction, and thus could not detect the influence of small changes in environmental and management variables on pasture productivity.     

Using structural equation modeling to investigate relationships among ecological variables

Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude –0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0.1258. Natural variability had a positive direct effect on biodiversity of magnitude 0.5347 and a negative indirect effect mediated through growth potential of magnitude –0.1105 yielding a positive total effects of magnitude 0.4242. Sediment contamination had a negative direct effect on biodiversity of magnitude –0.1956 and a negative indirect effect on growth potential via biodiversity of magnitude –0.067. Biodiversity had a positive effect on growth potential of magnitude 0.8432, and growth potential had a positive effect on biodiversity of magnitude 0.3398. The correlation between biodiversity and growth potential was estimated at 0.7658 and that between sediment contamination and natural variability at –0.3769.     

Minimizing the cost of translocation failure with decision‐tree models that predict species’ behavioral response in translocation sites

The high number of failures is one reason why translocation is often not recommended. Considering how behavior changes during translocations may improve translocation success. To derive decision‐tree models for species’ translocation, we used data on the short‐term responses of an endangered Australian skink in 5 simulated translocations with different release conditions. We used 4 different decision‐tree algorithms (decision tree, decision‐tree parallel, decision stump, and random forest) with 4 different criteria (gain ratio, information gain, gini index, and accuracy) to investigate how environmental and behavioral parameters may affect the success of a translocation. We assumed behavioral changes that increased dispersal away from a release site would reduce translocation success. The trees became more complex when we included all behavioral parameters as attributes, but these trees yielded more detailed information about why and how dispersal occurred. According to these complex trees, there were positive associations between some behavioral parameters, such as fight and dispersal, that showed there was a higher chance, for example, of dispersal among lizards that fought than among those that did not fight. Decision trees based on parameters related to release conditions were easier to understand and could be used by managers to make translocation decisions under different circumstances. Minimizar el Costo del Fracaso de la Reubicación con Modelos de Árboles de Decisión que Predigan la Respuesta Conductual de la Especie en los Sitios de Reubicación     

Rehabilitation of degraded community lands for sustainable development in Himalaya: a case study in Garhwal Himalaya,India

SUMMARY

An approach to the rehabilitation of degraded community lands built on people's perceptions and traditional knowledge was developed, implemented on a small scale (6 ha plot), and evaluated in terms of economic and ecological costs and benefits over a period of 5 years in a mid-altitude (1200 m) village of Garhwal Himalaya. Rehabilitation comprised establishment of water harvesting tanks, organic management of soil, agroforestry (native multipurpose trees t traditional crops), and decision making by the whole village community. Costs and benefits under irrigated and unirrigated conditions were compared. The total cost of establishing the irrigated agroforestry system was 1.23 fold that of the unirrigated one, whereas the total benefit was 2.09 fold. The average standing above-ground biomass of the 4-year-old plantation in the irrigated agroforestry system was 11.69 t/ha compared to 8.34 t/ha in the unirrigated system. Improvement in soil properties was more pronounced in the irrigated system than in the unirrigated one. Nutrient input, an input derived largely from forest biomass, in the unirrigated system was nearly 3 times higher than that in the irrigated system. It is concluded that, considering the local and national/regional/global interests in an integrated manner, agroforestry incorporating water management would be a more effective option for rehabilitating degraded community lands than the afforestation currently being attempted by the government in the mid-altitudes of Indian Himalaya.     

Movement and transformation of35S-labelled sulphate in the soil of a heavily polluted site in the Northern Czech Republic

Changes in chemistry and vertical distribution of³⁵S were investigated in column experiments using intact topsoil and repacked mineral soil horizons 1 to 20 weeks after tracer application (901 kBq³⁵S-SO₄ ^2– per column 6.5 cm in diameter). Horizons O, A, AE and Bvs of an Orthic Podzol were incubated at 20°C and wetted twice a week with 11 mm of natural throughfall precipitation (38.5 mg S0₄ ^2– L^–1-, pH 3.3). The top 35 cm of the soil contained 1,290 kg S ha^–1, or 18 times more than is the annual atmospheric S input (71.4 kg S ha^–1 yr^–1). Of this amount, 17.8 % was stored as inorganic sulphate S, 4.6 % as reduced inorganic S, and 77.6 % as organic S. In O + A and AE, free sulphate was the most abundant³⁵S form, while in Bvs the 35S activity of free and adsorbed sulphate was similar. The proportion of adsorbed sulphate increased with depth, averaging 23, 30 and 47 % of total inorganic sulphate³⁵S in O + A, AE and Bvs, respectively. Total specific activity of chemically transformed³⁵S (i.e., of reduced inorganic S and organic S) constituted 3.4, 3.8 and 105 % of inorganic sulphate³⁵S activity in O + A, AE and Bvs, respectively, in averaged weeks 2–4, and 7.5, 6.4 and 39.6 % in averaged weeks 11–13 in O+A, AE and Bvs, respectively. The turnover time of C-bonded³⁵S was shorter than that of ester sulphate³⁵S. An increase in FeS_2– ³⁵S with time indicated anaerobic conditions suitable for bacterial sulphate reduction. After 13 weeks, 68 % of the tracer was found deeper than 8 cm below soil surface.     

Catastrophic-like shifts in shallow Turkish lakes: a modeling approach

A generalized logistic model (GLM) was developed to determine occurrence of submerged macrophytes in shallow Lake Eymir, and the model was tested independently on the upstream shallow Lake Mogan using the data collected fortnightly from both lakes during 1997–2002. The independent variables included concentrations of chlorophyll-a (chl-a), suspended solids (SS) and total phosphorus (TP), Secchi disc transparency and z-scores of water levels. The dependent variable was the binary index of submerged plant occurrence. We used bootstrapping to determine the maximum number of epochs to train the model and to execute training when the corrected average cross entropy (c-index) leveled off. The model predicted that SS concentration, z-scores of water levels and TP concentration were the most important variables for determining occurrence of submerged plants. Sensitivity analyses showed that the probability of submerged plant occurrence followed a strong hysterisis response to varying water levels and the concentrations of SS and TP, with the break points being ±50 cm, 12–17 mg l⁻¹ and 200–300 μg l⁻¹, respectively. This observed sensitivity was in accordance with the alternative stable states hypothesis of shallow lakes. For occurrence of submerged plants, chlorophyll-a concentration and Secchi disc transparency had low significance. This was in concert with both relevances of input variables and the field results. The model gave a good definition of the system since the c-index and corrected c-index on the training data were high (0.970 and 0.963, respectively). Testing the model on Lake Mogan produced a c-index of 0.815 with around 80% of the cases being correctly classified. This showed that the model had a high ability to generalize over a spatially independent test set; therefore, it had a great reliability as well. In addition, the predictive power of the model was indeed very high. Consequently, the model captured the relationships between the input and output variables successfully and consistently with alternative stable states hypothesis.     

Structural path analysis of ecosystem networks

This paper explains the application of structural path analysis (SPA), an input–output-based technique for measuring flows in ecological and linked ecological–economic networks. Previous methods of input–output flow analysis have concluded with aggregate indexes relying on the summing feature of the Leontief inverse in order to completely account for throughflows along a multitude of inter-compartmental paths. This paper shows that for most linear dissipative networks, a manageable number of paths of limited length exist that cover in the order of 99% or more of total throughflow. These paths can be conveniently extracted, enumerated and ranked using SPA.     

Soil Quality Evolution After Land Use Change From Paddy Soil to Vegetable Land

A survey was done in 15 typical villages, 150 soil and 86 vegetable plant samples were taken in Jiaxin prefecture of the Taihu Lake region, northern Zhejian province. Results indicate that after 15–20 years land use changed from the paddy rice–wheat (or oilseed rape) double cropping system, to a continuous vegetable land has caused soil quality dramatic change. (1) Acidification: average soil pH was 5.4; about 61% of total samples were pH < 5.5. It was 0.9 units lower than 10 years ago with same upland vegetable cultivation and was 1.2 units lower than soil pH of paddy rice–wheat (or oilseed rape) rotation. (2) Fertilizer salt accumulation: the average salt content was 0.28%, among these about 36.2% of the total samples contained more than 0.3%. (3) Nitrate N and available phosphorus (P) over accumulation: on average it was 279 mg NO₃-N/kg, and 45–115 mg P/kg. Nitrate N four times higher and available P 4–10 times more than it is in present paddy rice–wheat rotation soils respectively. This has caused wide concern because of possible groundwater and well drinking water pollution by leached nitrate N and the P losses to water by runoff from vegetable lands induce surface water eutrophication.     

Planting History and Propagule Pressure as Predictors of Invasion by Woody Species in a Temperate Region

Abstract:  We studied 28 alien tree species currently planted for forestry purposes in the Czech Republic to determine the probability of their escape from cultivation and naturalization. Indicators of propagule pressure (number of administrative units in which a species is planted and total planting area) and time of introduction into cultivation were used as explanatory variables in multiple regression models. Fourteen species escaped from cultivation, and 39% of the variance was explained by the number of planting units and the time of introduction, the latter being more important. Species introduced early had a higher probability of escape than those introduced later, with more than 95% probability of escape for those introduced before 1801 and <5% for those introduced after 1892. Probability of naturalization was more difficult to predict, and eight species were misclassified. A model omitting two species with the largest influence on the model yielded similar predictors of naturalization as did the probability of escape. Both phases of invasion therefore appear to be driven by planting and introduction history in a similar way. Our results demonstrate the importance of forestry for recruitment of invasive trees. Six alien forestry trees, classified as invasive in the Czech Republic, are currently reported in nature reserves. In addition, forestry authorities want to increase the diversity of alien species and planting area in the country.     

Supply-side ecology of the brown mussel,<Emphasis Type="Italic"> Perna perna</Emphasis>: an investigation of spatial and temporal variation in,and coupling between,gamete release and larval supply

Sampling of recruitment-associated variables of Perna perna was done approximately monthly for 14 months at intertidal locations 500 m apart, nested within sites 25 km apart. Paired with intertidal locations were nearshore locations, 600 m to sea. Sampling assessed spawning, densities of larvae in the water column and densities of late plantigrades and juveniles on the shore. Major events in each variable were synchronous over larger scales (10s of kilometres) while subsidiary events were synchronised at smaller scales, varying within sites (100s of metres) or even within locations (metres). This suggests that the processes driving major events operated over large scales while processes operating at much more local scales drove less intense, more localised events. A major spawning event occurred at all locations in May–June 1998. Weaker spawning events occurred at different times in different locations. Larvae were found on 80% of sampling occasions, densities peaking in January–March 1998 and 1999 at all locations. Plantigrades and juveniles showed less clear patterns, with considerable residual variation. There was no sign of strong coupling among variables with few significant direct or cross correlations. The major sources of variability shifted from time to space as one progressed from spawning, to plantigrade density to juvenile density. For spawning, time was the most important source (58%) of heterogeneity and space accounted for little (8%) of the total variance. For larvae and late plantigrades, time was still the most important source of variability (41% and 33%, respectively), but space was a much more substantial component. For juveniles, small-scale (residual) spatial variability dominated total variability (75%). This strongly suggests the importance of hydrography and its effects on variation in delivery of larvae to the intertidal from offshore. These findings also indicate greater spatial heterogeneity as recruits age, reflecting small-scale variations in larval delivery and the increasing importance of post-settlement mortality.Communicated by G.F. Humphrey, Sydney     

Bridging the gap between ecophysiological and genetic knowledge to assess the adaptive potential of European beech

In this study we aimed to combine knowledge of the ecophysiology and genetics of European beech to assess the potential of this species to adapt to environmental change. Therefore, we performed field and experimental studies on the genetic and ecophysiological functioning of beech. This information was integrated through a coupled genetic–ecophysiological model for individual trees that was parameterized with information derived from our own studies or from the literature. Using the model, we evaluated the adaptive response of beech stands in two ways: firstly, through sensitivity analyses (of initial genetic diversity, pollen dispersal distance, heritability of selected phenotypic traits, and forest management, representing disturbances) and secondly, through the evaluation of the responses of phenotypic traits and their genetic diversity to four management regimes applied to 10 study plots distributed over Western Europe. The model results indicate that the interval between recruitment events strongly affects the rate of adaptive response, because selection is most severe during the early stages of forest development. Forest management regimes largely determine recruitment intervals and thereby the potential for adaptive responses. Forest management regimes also determine the number of mother trees that contribute to the next generation and thereby the genetic variation that is maintained. Consequently, undisturbed forests maintain the largest amount of genetic variation, as recruitment intervals approach the longevity of trees and many mother trees contribute to the next generation. However, undisturbed forests have the slowest adaptive response, for the same reasons.Gene flow through pollen dispersal may compensate for the loss in genetic diversity brought about by selection. The sensitivity analysis showed that the total genetic diversity of a 2 ha stand is not affected by gene flow if the pollen distance distribution is varied from highly left-skewed to almost flat. However, a stand with a prevailing short-distance gene flow has a more pronounced spatial genetic structure than stands with equal short- and long-distance gene flows. The build-up of a spatial genetic structure is also strongly determined by the recruitment interval. Overall, the modelling results indicate that European beech has high adaptive potential to environmental change if recruitment intervals are short and many mother trees contribute to the next generation.The findings have two implications for modelling studies on the impacts of climate change on forests. Firstly: it cannot be taken for granted that parameter values remain constant over a time horizon of even a few generations – this is particularly important for threshold values subject to strong selection, like budburst, frost hardiness, drought tolerance, as used in species area models. Secondly: forest management should be taken into account in future assessments, as management affects the rate of adaptive response and thereby the response on trees and forests to environmental change, and because few forests are unmanaged. We conclude that a coupled ecophysiological and quantitative genetic tree model is a useful tool for such studies.     

The role of local and landscape level measures of greenness in modelling boreal plant species richness

Measurements of primary productivity and its heterogeneity based on satellite images can provide useful estimates of species richness and distribution patterns. However, species richness at a given site may depend not only on local habitat quality and productivity but also on the characteristics of the surrounding landscape. In this study we investigated whether the predictions of species richness of plant families in northern boreal landscape in Finland can be improved by incorporating greenness information from the surrounding landscape, as derived from remotely sensed data (mean, maximum, standard deviation and range values of NDVI derived from Landsat ETM), into local greenness models. Using plant species richness data of 28 plant families from 440 grid cells of 25 ha in size, generalized additive models (GAMs) were fitted into three different sets of explanatory variables: (1) local greenness only, (2) landscape greenness only, and (3) combined local and landscape greenness. The derived richness–greenness relationships were mainly unimodal or positively increasing but varied between different plant families, and depended also on whether greenness was measured as mean or maximum greenness. Incorporation of landscape level greenness variables improved significantly both the explanatory power and cross-validation statistics of the models including only local greenness variables. Landscape greenness information derived from remote sensing data integrated with local information has thus the potentiality to improve predictive assessments of species richness over extensive and inaccessible areas, especially in high-latitude landscapes. Overall, the significant relationship between plants and surrounding landscape quality detected here suggests that landscape factors should be considered in preserving species richness of boreal environments, as well as in conservation planning for biodiversity in other environments.     

SEIB–DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individual-based approach

We report the development of a new spatially explicit individual-based Dynamic Global Vegetation Model (SEIB–DGVM), the first DGVM that can simulate the local interactions among individual trees within a spatially explicit virtual forest. In the model, a sample plot is placed at each grid box, and then the growth, competition, and decay of each individual tree within each plot is calculated by considering the environmental conditions for that tree as it relates to the trees that surround it. Based on these parameters only, the model simulated time lags between climate change and vegetation change. This time lags elongated when original biome was forest, because existing trees prevent newly establish trees from receiving enough sunlight and space to quickly replace the original vegetation. This time lags also elongated when horizontal heterogeneity of sunlight distribution was ignored, indicating the potential importance of horizontal heterogeneity for predicting transitional behavior of vegetation under changing climate. On a local scale, the model reproduced climate zone-specific patterns of succession, carbon dynamics, and water flux, although on a global scale, simulations were not always in agreement with observations. Because the SEIB–DGVM was formulated to the scale at which field biologists work, the measurements of relevant parameters and data comparisons are relatively straightforward, and the model should enable more robust modeling of terrestrial ecosystems.