Sources of Variability in Net Primary Production Predictions at a Regional Scale: A Comparison Using PnET-II and TEM 4.0 in Northeastern US Forests

Because model predictions at continental and global scales are necessarily based on broad characterizations of vegetation, soils, and climate, estimates of carbon stocks and fluxes made by global terrestrial biosphere models may not be accurate for every region. At the regional scale, we suggest that attention can be focused more clearly on understanding the relative strengths of predicted net primary productivity (NPP) limitation by energy, water, and nutrients. We evaluate the sources of variability among model predictions of NPP with a regional-scale comparison between estimates made by PnET-II (a forest ecosystem process model previously applied to the northeastern region) and TEM 4.0 (a terrestrial biosphere model typically applied to the globe) for the northeastern US. When the same climate, vegetation, and soil data sets were used to drive both models, regional average NPP predictions made by PnET-II and TEM were remarkably similar, and at the biome level, model predictions agreed fairly well with NPP estimates developed from field measurements. However, TEM 4.0 predictions were more sensitive to regional variations in temperature as a result of feedbacks between temperature and belowground N availability. In PnET-II, the direct link between transpiration and photosynthesis caused substantial water stress in hardwood and pine forest types with increases in solar radiation; predicted water stress was relieved substantially when soil water holding capacity (WHC) was increased. Increasing soil WHC had little effect on TEM 4.0 predictions because soil water storage was already sufficient to meet plant demand with baseline WHC values, and because predicted N availability under baseline conditions in this region was not limited by water. Because NPP predictions were closely keyed to forest cover type, the relative coverage of low- versus high-productivity forests at both fine and coarse resolutions was an important determinant of regional NPP predictions. Therefore, changes in grid cell size and differences in the methods used to aggregate from fine to coarse resolution were important to NPP predictions insofar as they changed the relative proportions of forest cover. We suggest that because the small patches of high-elevation spruce-fir forest in this region are substantially less productive than forests in the remainder of the region, more accurate NPP predictions will result if models applied to this region use land cover input data sets that retain as much fine-resolution forest type variability as possible. The differences among model responses to variations in climate and soil WHC data sets suggest that the models will respond quite differently to scenarios of future climate. A better understanding of the dynamic interactions between water stress, N availability, and forest productivity in this region will enable models to make more accurate predictions of future carbon stocks and fluxes. Received 19 June 1998; accepted 25 June 1999.     

Spatial Uncertainty and Ecological Models

Applied ecological models that are used to understand and manage natural systems often rely on spatial data as input. Spatial uncertainty in these data can propagate into model predictions. Uncertainty analysis, sensitivity analysis, error analysis, error budget analysis, spatial decision analysis, and hypothesis testing using neutral models are all techniques designed to explore the relationship between variation in model inputs and variation in model predictions. Although similar methods can be used to answer them, these approaches address different questions. These approaches differ in (a) whether the focus is forward or backward (forward to evaluate the magnitude of variation in model predictions propagated or backward to rank input parameters by their influence); (b) whether the question involves model robustness to large variations in spatial pattern or to small deviations from a reference map; and (c) whether processes that generate input uncertainty (for example, cartographic error) are of interest. In this commentary, we propose a taxonomy of approaches, all of which clarify the relationship between spatial uncertainty and the predictions of ecological models. We describe existing techniques and indicate a few areas where research is needed.     

Estimating carbon carrying capacity in natural forest ecosystems across heterogeneous landscapes: addressing sources of error

Evaluating contributions of forest ecosystems to climate change mitigation requires well‐calibrated carbon cycle models with quantified baseline carbon stocks. An appropriate baseline for carbon accounting of natural forests at landscape scales is carbon carrying capacity (CCC); defined as the mass of carbon stored in an ecosystem under prevailing environmental conditions and natural disturbance regimes but excluding anthropogenic disturbance. Carbon models require empirical measurements for input and calibration, such as net primary production (NPP) and total ecosystem carbon stock (equivalent to CCC at equilibrium). We sought to improve model calibration by addressing three sources of errors that cause uncertainty in carbon accounting across heterogeneous landscapes: (1) data‐model representation, (2) data‐object representation, (3) up‐scaling. We derived spatially explicit empirical models based on environmental variables across landscape scales to estimate NPP (based on a synthesis of global site data of NPP and gross primary productivity, n=27), and CCC (based on site data of carbon stocks in natural eucalypt forests of southeast Australia, n=284). The models significantly improved predictions, each accounting for 51% of the variance. Our methods to reduce uncertainty in baseline carbon stocks, such as using appropriate calibration data from sites with minimal human disturbance, measurements of large trees and incorporating environmental variability across the landscape, have generic application to other regions and ecosystem types. These analyses resulted in forest CCC in southeast Australia (mean total biomass of 360 t C ha^?1, with cool moist temperate forests up to 1000 t C ha^?1) that are larger than estimates from other national and international (average biome 202 t C ha^?1) carbon accounting systems. Reducing uncertainty in estimates of carbon stocks in natural forests is important to allow accurate accounting for losses of carbon due to human activities and sequestration of carbon by forest growth.     

Uncertainty in Calculations of Net Primary Production for Grasslands

Net primary production (NPP) is a fundamental characteristic of all ecosystems and foundational to understanding the fluxes of energy and nutrients. Because NPP cannot be measured directly, researchers use field-measured surrogates as input variables in various equations designed to estimate ‘true NPP’. This has led to considerable debate concerning which equations most accurately estimate ‘true NPP’. This debate has influenced efforts to assess NPP in grasslands, with researchers often advocating more complex equations to avoid underestimation. However, this approach ignores the increase in statistical error associated with NPP estimates as a greater number of parameters and more complex mathematical functions are introduced into the equation. Using published grassland data and Monte Carlo simulation techniques, we assessed the relative variability in NPP estimates obtained using six different NPP estimation equations that varied in both the number of parameters and intricacy of mathematical operations. Our results indicated that more complex equations may result in greater uncertainty without reducing the probability of underestimation. The amount of uncertainty associated with estimates of NPP was influenced by the number of parameters as well as the variability in the data and the nature of the mathematical operations. For example, due to greater variability in the field-measured belowground data than aboveground data, estimates of belowground NPP tended to have more uncertainty than estimates of aboveground NPP. An analysis in which the input data were standardized allowed us to isolate the details of the calculations from the variability in the data in assessing the propagation of uncertainty. This analysis made clear that equations with product terms have the potential to magnify the uncertainty of the inputs in the estimates of NPP although this relationship was complicated by interactions with data variability and number of parameters. Our results suggest that more complex NPP estimation equations can increase uncertainty without necessarily reducing risk of underestimation. Because estimates can never be tested by comparison to “true NPP”, we recommend that researchers include an assessment of propagation of statistical error when evaluating the ‘best’ estimation method.     

A historical meta‐analysis of global terrestrial net primary productivity: are estimates converging?

Net primary productivity (NPP) is one of the most important ecosystem parameters, representing vegetation activity, biogeochemical cycling, and ecosystem services. To assess how well the scientific community understands the biospheric function, a historical meta‐analysis was conducted. By surveying the literature from 1862 to 2011, I extracted 251 estimates of total terrestrial NPP at the present time (NPP_T) and calculated their statistical metrics. For all the data, the mean±standard deviation and median were 56.2±14.3 and 56.4 Pg C yr^–1, respectively. Even for estimates published after 2000, a substantial level of uncertainty (coefficient of variation by ±15%) was inevitable. The estimates were categorized on the basis of methodology (i.e., inventory analysis, empirical model, biogeochemical model, dynamic global vegetation model, and remote sensing) to examine the consistency among the statistical metrics of each category. Chronological analysis revealed that the present NPP_T estimates were directed by extensive field surveys in the 1960s and 1970s (e.g., the International Biological Programme). A wide range of uncertainty remains in modern estimates based on advanced biogeochemical and dynamic vegetation models and remote‐sensing techniques. Several critical factors accounting for the estimation uncertainty are discussed. Ancillary analyses were performed to derive additional ecological and human‐related parameters related to NPP. For example, interannual variability, carbon‐use efficiency (a ratio of NPP to gross photosynthesis), human appropriation, and preindustrial NPP_T were assessed. Finally, I discuss the importance of improving NPP_T estimates in the context of current global change studies and integrated carbon cycle research.     

Incorporating uncertainty in predictive species distribution modelling

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.     

Does the growth response of woody plants to elevated CO2 increase with temperature? A model‐oriented meta‐analysis

The temperature dependence of the reaction kinetics of the Rubisco enzyme implies that, at the level of a chloroplast, the response of photosynthesis to rising atmospheric CO₂ concentration (C_a) will increase with increasing air temperature. Vegetation models incorporating this interaction predict that the response of net primary productivity (NPP) to elevated CO₂ (eC_a) will increase with rising temperature and will be substantially larger in warm tropical forests than in cold boreal forests. We tested these model predictions against evidence from eC_a experiments by carrying out two meta‐analyses. Firstly, we tested for an interaction effect on growth responses in factorial eC_a × temperature experiments. This analysis showed a positive, but nonsignificant interaction effect (95% CI for above‐ground biomass response = ?0.8, 18.0%) between eC_a and temperature. Secondly, we tested field‐based eC_a experiments on woody plants across the globe for a relationship between the eC_a effect on plant biomass and mean annual temperature (MAT). This second analysis showed a positive but nonsignificant correlation between the eC_a response and MAT. The magnitude of the interactions between CO₂ and temperature found in both meta‐analyses were consistent with model predictions, even though both analyses gave nonsignificant results. Thus, we conclude that it is not possible to distinguish between the competing hypotheses of no interaction vs. an interaction based on Rubisco kinetics from the available experimental database. Experiments in a wider range of temperature zones are required. Until such experimental data are available, model predictions should aim to incorporate uncertainty about this interaction.     

Predicting ecosystem dynamics at regional scales: an evaluation of a terrestrial biosphere model for the forests of northeastern North America

Terrestrial biosphere models are important tools for diagnosing both the current state of the terrestrial carbon cycle and forecasting terrestrial ecosystem responses to global change. While there are a number of ongoing assessments of the short-term predictive capabilities of terrestrial biosphere models using flux-tower measurements, to date there have been relatively few assessments of their ability to predict longer term, decadal-scale biomass dynamics. Here, we present the results of a regional-scale evaluation of the Ecosystem Demography version 2 (ED2)-structured terrestrial biosphere model, evaluating the model's predictions against forest inventory measurements for the northeast USA and Quebec from 1985 to 1995. Simulations were conducted using a default parametrization, which used parameter values from the literature, and a constrained model parametrization, which had been developed by constraining the model's predictions against 2 years of measurements from a single site, Harvard Forest (42.5° N, 72.1° W). The analysis shows that the constrained model parametrization offered marked improvements over the default model formulation, capturing large-scale variation in patterns of biomass dynamics despite marked differences in climate forcing, land-use history and species-composition across the region. These results imply that data-constrained parametrizations of structured biosphere models such as ED2 can be successfully used for regional-scale ecosystem prediction and forecasting. We also assess the model's ability to capture sub-grid scale heterogeneity in the dynamics of biomass growth and mortality of different sizes and types of trees, and then discuss the implications of these analyses for further reducing the remaining biases in the model's predictions.     

Terrestrial net primary production estimates for 0.5° grid cells from field observations—a contribution to global biogeochemical modeling

Net Primary Production (NPP) is an important component of the carbon cycle and, among the pools and fluxes that make up the cycle, it is one of the steps that are most accessible to field measurement. While easier than some other steps to measure, direct measurement of NPP is tedious and not practical for large areas and so models are generally used to study the carbon cycle at a global scale. Nevertheless these models require field measurements of NPP for parameterization, calibration and validation. Most NPP data are for relatively small field plots that cannot represent the 0.5° × 0.5° grid cells that are commonly used in global scale models. Furthermore, technical difficulties generally restrict NPP measurements to aboveground parts and sometimes do not even include all components of aboveground NPP. Thus direct inter‐comparison between field data obtained in different studies or comparison of these results with coarse resolution model outputs can be misleading. We summarize and present a series of methods that were used by original authors to estimate NPP and how and what we have done to prepare a consistent data set of NPP for 0.5 °grid cells for a range of biomes from these studies. The methods used for estimation of NPP include: (i) aggregation of fine‐scale (plot or stand‐level) vegetation inventory data to larger grid cells, (ii) mapping of grid cells and area weighting of field NPP observations in each mapped class, (iii) direct correlation of extensive data sets of ground measurements with remotely sensed spectral vegetation indices, (iv) local modeling of NPP using key independent variables, for which maps are available at the scale of the grid cell, and (v) regression analysis to link productivity with controlling environmental variables. For a few grid cells whose NPP were obtained for multiple years, temporal analysis was conducted. The grid cells are grouped to the biome level and are compared with existing compilations of field NPP and the results of the Miami potential NPP model. Mean NPP was similar to the well‐known compilation of Whittaker and Likens, except for temperate evergreen needle‐leaved forest, woodland, and shrubland. The grid cell datasets are a contribution to the International Geosphere‐Biosphere Programme (IGBP) Data and Information System (DIS) Global Primary Production Data Initiative (GPPDI). The full dataset currently contains 3654 cells (including replicate measurements) developed from 15 studies representing NPP in croplands, sparse vegetation, shrub lands, grasslands, and forests worldwide. An edited subset consists of 2335 cells in which outliers were removed and all replicate measurements were averaged for each unique geographical location. Most of the data incorporated into GPPDI were wholly or partly developed by participants in the GPPDI, in addition to the present authors. These studies are gathered together here to provide a consistent account of the grid cell component of GPPDI and an analysis of the entire data set. The datasets have been deposited in an IGBP‐DIS GPPDI database ( http://daacl.esd.ornl.gov/npq/GPPDI/Combined_GPPDI_des.html ).     

Permutation tests for phylogenetic comparative analyses of high‐dimensional shape data: What you shuffle matters

Evaluating statistical trends in high‐dimensional phenotypes poses challenges for comparative biologists, because the high‐dimensionality of the trait data relative to the number of species can prohibit parametric tests from being computed. Recently, two comparative methods were proposed to circumvent this difficulty. One obtains phylogenetic independent contrasts for all variables, and statistically evaluates the linear model by permuting the phylogenetically independent contrasts (PICs) of the response data. The other uses a distance‐based approach to obtain coefficients for generalized least squares models (D‐PGLS), and subsequently permutes the original data to evaluate the model effects. Here, we show that permuting PICs is not equivalent to permuting the data prior to the analyses as in D‐PGLS. We further explain why PICs are not the correct exchangeable units under the null hypothesis, and demonstrate that this misspecification of permutable units leads to inflated type I error rates of statistical tests. We then show that simply shuffling the original data and recalculating the independent contrasts with each iteration yields significance levels that correspond to those found using D‐PGLS. Thus, while summary statistics from methods based on PICs and PGLS are the same, permuting PICs can lead to strikingly different inferential outcomes with respect to statistical and biological inferences.     

Using models to guide field experiments: a priori predictions for the CO2 response of a nutrient‐ and water‐limited native Eucalypt woodland

The response of terrestrial ecosystems to rising atmospheric CO₂ concentration (C_a), particularly under nutrient‐limited conditions, is a major uncertainty in Earth System models. The Eucalyptus Free‐Air CO₂ Enrichment (EucFACE) experiment, recently established in a nutrient‐ and water‐limited woodland presents a unique opportunity to address this uncertainty, but can best do so if key model uncertainties have been identified in advance. We applied seven vegetation models, which have previously been comprehensively assessed against earlier forest FACE experiments, to simulate a priori possible outcomes from EucFACE. Our goals were to provide quantitative projections against which to evaluate data as they are collected, and to identify key measurements that should be made in the experiment to allow discrimination among alternative model assumptions in a postexperiment model intercomparison. Simulated responses of annual net primary productivity (NPP) to elevated C_a ranged from 0.5 to 25% across models. The simulated reduction of NPP during a low‐rainfall year also varied widely, from 24 to 70%. Key processes where assumptions caused disagreement among models included nutrient limitations to growth; feedbacks to nutrient uptake; autotrophic respiration; and the impact of low soil moisture availability on plant processes. Knowledge of the causes of variation among models is now guiding data collection in the experiment, with the expectation that the experimental data can optimally inform future model improvements.     

The Heritability of Difference Scores when Environments are Correlated

The statistical analysis of difference scores (contrasts) is a fundamental problem in all learning, feeding, and training experiments and tests, and in longitudinal studies of growth and development. Outgoing from the analogy between the mathematical models of classical psychological test theory and quantitative genetics, as well as between the parameters reliability and heritability of these models, the present paper derives the formulas of the heritability of difference scores in general cases where it is not assumed that environmental deviations on distinct tests and measurements are uncorrelated. Contrary to the assertion, made by FELDMAN and LEWONTIN , heritabilities in the broad sense can be used as ideal weighting factors in long-range personnel index selection. Longitudinal studies of twins and the cotwin method are powerful experimental designs to estimate heritabilities of differences.     

火干扰与生态系统的碳循环

火干扰是陆地生态系统碳循环的重要影响因子。它改变着整个系统的碳循环过程与碳分布格局。正确评估火干扰在碳循环过程中的作用,对推进全球碳循环研究有着重要的意义。从4个方面系统的回顾了火干扰对碳循环的影响过程及其研究方法:(1)火烧过程中含碳痕量气体排放的估算;(2)火烧迹地恢复过程中净第一性生产力(NPP)与土壤呼吸的变化;(3)火干扰对生态系统碳源/汇的影响;(4)模型方法在火干扰与生态系统碳循环研究中的应用。目前火灾碳排量的估算方法业已成熟,但进行更精确的估算必须基于对受干扰生态系统的性质以及火势的时空变异性质的准确理解;相比之下,对于间接的、更为重要的影响,即对火烧迹地恢复过程中碳循环变化的研究则显不足。由于数据缺乏,现有研究大多限于对碳循环某一方面的观测与定量描述,缺乏全面的机理性分析。对此,实地观测、模型模拟与遥感观测的跨尺度集成将成为未来火干扰研究的一个主要方向。     

What processes must we understand to forecast regional-scale population dynamics?

An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.     

Predictive ADMET studies, the challenges and the opportunities

Predictive ADMET is the new 'hip' area in drug discovery. The aim is to use large databases of ADMET data associated with structures to build computational models that link structural changes with changes in response, from which compounds with improved properties can be designed and predicted. These databases also provide the means to enable predictions of human ADMET properties to be made from human in vitro and animal in vivo ADMET measurements. Both methods are limited by the amount of data available to build such predictive models, the limitations of modelling methods and our understanding of the systems we wish to model. The current failures, successes and opportunities are reviewed.     

A statistical approach to quasi-extinction forecasting

Forecasting population decline to a certain critical threshold (the quasi-extinction risk) is one of the central objectives of population viability analysis (PVA), and such predictions figure prominently in the decisions of major conservation organizations. In this paper, we argue that accurate forecasting of a population's quasi-extinction risk does not necessarily require knowledge of the underlying biological mechanisms. Because of the stochastic and multiplicative nature of population growth, the ensemble behaviour of population trajectories converges to common statistical forms across a wide variety of stochastic population processes. This paper provides a theoretical basis for this argument. We show that the quasi-extinction surfaces of a variety of complex stochastic population processes (including age-structured, density-dependent and spatially structured populations) can be modelled by a simple stochastic approximation: the stochastic exponential growth process overlaid with Gaussian errors. Using simulated and real data, we show that this model can be estimated with 20-30 years of data and can provide relatively unbiased quasi-extinction risk with confidence intervals considerably smaller than (0,1). This was found to be true even for simulated data derived from some of the noisiest population processes (density-dependent feedback, species interactions and strong age-structure cycling). A key advantage of statistical models is that their parameters and the uncertainty of those parameters can be estimated from time series data using standard statistical methods. In contrast for most species of conservation concern, biologically realistic models must often be specified rather than estimated because of the limited data available for all the various parameters. Biologically realistic models will always have a prominent place in PVA for evaluating specific management options which affect a single segment of a population, a single demographic rate, or different geographic areas. However, for forecasting quasi-extinction risk, statistical models that are based on the convergent statistical properties of population processes offer many advantages over biologically realistic models.     

应用BioMod集成多种模型研究物种的空间分布——以铁杉在中国的潜在分布为例

新型统计方法和多源、多尺度空间信息数据的产生促进了物种空间分布模型的快速发展。不同的物种空间分布模型在生态学理论的运用以及前提假设上存在差异。选用不同的模型方法和输入数据会带来预测结果的不确定性。对比并集成多个物种空间分布模型,同时利用多组输入数据可降低预测的不确定性,提高物种分布模拟的精度。本文以中国特有种铁杉（Tsuga chinensis）为例,运用基于R语言开发的BioMod软件包对比9个物种空间分布模型对铁杉的模拟效果。最后以曲线下面积（ROC）为权重集成9个模型的模拟结果,产生和筛选最佳的铁杉潜在空间分布图。研究发现随机森林模型（RF）的模拟效果最好,其次是多元适应回归样条函数模型（MARS）和广义相加模型（GAM）,模拟效果最差的是表面分布区分室模型（SRE）。模型集成结果显示,最适宜铁杉分布的区域集中在中国的西南及四川盆地周围,其次零星分散于华南和台湾部分地区。这一结果与前人对铁杉自然分布的描述和研究结果较为吻合。研究进一步表明,通过模型的集成能有效地降低由于单个模型所带来的模拟结果不确定性,从而提高模拟的精度和效果。     

Template CoMFA Generates Single 3D-QSAR Models that,for Twelve of Twelve Biological Targets,Predict All ChEMBL-Tabulated Affinities

The possible applicability of the new template CoMFA methodology to the prediction of unknown biological affinities was explored. For twelve selected targets, all ChEMBL binding affinities were used as training and/or prediction sets, making these 3D-QSAR models the most structurally diverse and among the largest ever. For six of the targets, X-ray crystallographic structures provided the aligned templates required as input (BACE, cdk1, chk2, carbonic anhydrase-II, factor Xa, PTP1B). For all targets including the other six (hERG, cyp3A4 binding, endocrine receptor, COX2, D2, and GABAa), six modeling protocols applied to only three familiar ligands provided six alternate sets of aligned templates. The statistical qualities of the six or seven models thus resulting for each individual target were remarkably similar. Also, perhaps unexpectedly, the standard deviations of the errors of cross-validation predictions accompanying model derivations were indistinguishable from the standard deviations of the errors of truly prospective predictions. These standard deviations of prediction ranged from 0.70 to 1.14 log units and averaged 0.89 (8x in concentration units) over the twelve targets, representing an average reduction of almost 50% in uncertainty, compared to the null hypothesis of “predicting” an unknown affinity to be the average of known affinities. These errors of prediction are similar to those from Tanimoto coefficients of fragment occurrence frequencies, the predominant approach to side effect prediction, which template CoMFA can augment by identifying additional active structural classes, by improving Tanimoto-only predictions, by yielding quantitative predictions of potency, and by providing interpretable guidance for avoiding or enhancing any specific target response.     

陆地植被净初级生产力计算模型研究进展

植被净初级生产力(NPP)研究是全球变化与陆地生态系统的核心内容之一。在回顾NPP模型研究的基础上,综合分析了气候模型、生态生理过程模型、光能利用率模型各自的优缺点,并对NPP模型研究做出展望。生态生理过程模型是当前陆地NPP估算研究的主要手段,而区域尺度转换则是它所面临的关键问题。近年来光能利用率模型已成为NPP估算的一种全新手段,它利用遥感所获得的全覆盖数据,使区域及全球尺度的NPP估算成为可能,但其生态学机理还有待于进一步研究。已有研究表明,“生态一遥感耦合模型”将是陆地NPP估算的主要发展方向,它融合了生态生理过程模型和光能利用率模型的优点,增强了NPP模型估算的可靠性和可操作性。     

Cadmium and copper accumulation in the common mussel Mytilus edulis in the Western Scheldt estuary: a model approach

The Western Scheldt of the Dutch Delta area is severely contaminated with trace metals. Accumulation models of trace metals in the mussel Mytilus edulis are required to predict the biological efficiency of reductions in the metal and organic matter load. Two models are constructed: a black-box model and a physiologically structured model. The black-box model predicts metal accumulation in mussels from uptake and elimination parameters. The physiological model attempts to improve predictions by taking into account the kinetics of individual uptake and elimination routes. These in turn, are taken as depending upon two more general physiological processes, the ventilation rate and the metabolic rate. Metal uptake via food and water are expressed as relative fractions. Metal input is differentiated into particulate adsorbed, and dissolved species.The reliability of the two models is evaluated by comparing predicted concentrations for mussels with measurements. Model predictions for copper deviate less than 100% from measured concentrations, but neither model appears to predict cadmium concentration with sufficient accuracy since deviations of more than 100% occured. The introduction of physiological refinements did not improve performance. Food mediated contributions for cadmium and copper to total body burden had been overestimated in the model by a factor of 100 when compared to literature values. The physiological model did predict that the ratio of food mediated contribution to total body burden is probably different for cadmium and copper and decreases with increasing salinity for both. As yet there are no measurements available to confirm such predictions.We conclude that additional laboratory experiments should be done for a better understanding of why there is poor agreement between the few field observations and the simulations. In these experiments mussels grown under different environmental condition can be tested for their accumulation capacity of trace metals. More field observations are needed.