Operational Effect Variables and Functional Ecosystem Classifications – a Review on Empirical Models for Aquatic Systems along a Salinity Gradient

This paper presents a comparative study based on a very comprehensive set of empirical data from many international data bases including fresh water systems, coastal brackish water areas and marine coastal areas. We present a general trophic level classification system (oligotrophic, mesotrophic, eutrophic and hypertrophic categories) for sites/areas characterised by a wide range of salinities. This classification system targets on the following operational effect variables (bioindicators), which are meant to reflect key structural and functional aspects of aquatic ecosystems and characteristic (median) values for entire defined areas (the ecosystem scale) for the growing season: Secchi depth (as a standard measure of water clarity), chlorophyll‐a concentrations (a measure of primary phytoplankton biomass), the oxygen saturation in the deep‐water zone (an indicator reflecting sedimentation, oxygen consumption, oxygen concentrations and the habitat conditions for zoobenthos, an important functional group) and the macrophyte cover (an important variable for the bioproduction potential, including fish production, and the “biological value” of aquatic systems). For a wide range of systems, these bioindicators can be predicted using practically useful models, i.e., models based on variables that can be accessed from standard monitoring programs and maps. These bioindicators are regulated by a set of abiotic factors, such as salinity, suspended particulate matter (SPM), nutrient concentrations (N and P), morphometry and water exchange. Empirical data ultimately form the basis for most ecological/environmental studies and this work uses maybe the most comprehensive data set ever related to trophic level conditions. It also gives compilations of empirically‐based (statistical) models quantifying how the variables are interrelated and how they reflect fundamental aspects of aquatic ecosystems. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Drought controls over conductance and assimilation of a Mediterranean evergreen ecosystem: scaling from leaf to canopy

Drought control over conductance and assimilation was assessed using eddy flux and meteorological data monitored during four summer periods from 1998 to 2001 above a closed canopy of the Mediterranean evergreen oak tree Quercus ilex. Additional discrete measurements of soil water content and predawn leaf water potential were used to characterize the severity of the drought. Canopy conductance was estimated through the big‐leaf approach of Penman–Monteith by inverting latent heat fluxes. The gross primary production ( GPP ) was estimated by adding ecosystem respiration to net ecosystem exchange. Ecosystem respiration was deduced from night flux when friction velocity ( u _*) was greater than 0.35 m s^?1. Empirical equations were identified that related maximal canopy conductance and daily ecosystem GPP to relative soil water content ( RWC) , the ratio of current soil water content to the field capacity, and to the predawn leaf water potential. Both variables showed a strong decline with soil RWC for values lower than 0.7. The sharpest decline was observed for GPP . The curves reached zero for RWC =0.41 and 0.45 for conductance and GPP , respectively. When the predawn leaf water potential was used as a surrogate for soil water potential, both variables showed a hyperbolic decline with decreasing water potential. These results were compared with already published literature values obtained at leaf level from the same tree species. Scaling up from the leaf to ecosystem highlighted the limitation of two big‐leaf representations: Penman–Monteith and Sellers' Π factor. Neither held completely for comparing leaf and canopy fluxes. Tower measurements integrate fluxes from foliage elements clumped at several levels of organization: branch, tree, and ecosystem. The Q. ilex canopy exhibited non‐random distribution of foliage, emphasizing the need to take into account a clumping index, the factor necessary to apply the Lambert–Beer law to natural forests. Our results showed that drought is an important determinant in water losses and CO₂ fluxes in water‐limited ecosystems. In spite of the limitations inherent to the big‐leaf representation of the canopy, the equations are useful for predicting the influence of environmental factors in Mediterranean woodlands and for interpreting ecosystem exchange measurements.     

Variability in nitrogen stable isotope ratios of macroalgae: consequences for the identification of nitrogen sources

In our research, we collected and analyzed numerous macroalgal specimens (738) for isotopic analysis sampled over a year at monthly intervals across 20 sites within the Urías lagoon complex, a typical subtropical coastal ecosystem located in the Gulf of California. We quantified and characterized (chemically and isotopically) the N loads received by Urías throughout a year. We studied the spatial‐temporal variation of the chemical forms and isotopic signals of the available N in the water column, and we monitored in situ different environmental variables and other hydrodynamic parameters. Multiple N sources (e.g., atmospheric, sewage, seafood processing, agriculture and aquaculture effluents) and biogeochemical reactions related to the N cycle (e.g., ammonia volatilization, nitrification and denitrification) co‐occurring across the ecosystem, result in a mixture of chemical species and isotopic compositions of available N in the water column. Increased variability was observed in the δ¹⁵N values of macroalgae (0.41‰–22.67‰). Based on our results, the variation in δ¹⁵N was best explained by spatio‐temporal changes in available N and not necessarily related to the N sources. The variability was also explained by the differences in macroalgal biology among functional groups, species and/or individuals. Although the δ¹⁵N‐macroalgae technique was a useful tool to identify N sources, its application in coastal ecosystems receiving multiple N sources, with changing environmental conditions influencing biogeochemical processes, and high diversity of ephemeral macroalgal species, could be less sensitive and have less predictive power.     

Combining field experiments and predictive models to assess potential for increased plant diversity to climate‐proof intensive agriculture

Agricultural production systems face increasing threats from more frequent and extreme weather fluctuations associated with global climate change. While there is mounting evidence that increased plant community diversity can reduce the variability of ecosystem functions (such as primary productivity) in the face of environmental fluctuation, there has been little work testing whether this is true for intensively managed agricultural systems. Using statistical modeling techniques to fit environment–productivity relationships offers an efficient means of leveraging hard‐won experimental data to compare the potential variability of different mixtures across a wide range of environmental contexts. We used data from two multiyear field experiments to fit climate–soil–productivity models for two pasture mixtures under intensive grazing—one composed of two drought‐sensitive species (standard), and an eight‐species mixture including several drought‐resistant species (complex). We then used these models to undertake a scoping study estimating the mean and coefficient of variation (CV) of annual productivity for long‐term climate data covering all New Zealand on soils with low, medium, or high water‐holding capacity. Our results suggest that the complex mixture is likely to have consistently lower CV in productivity, irrespective of soil type or climate regime. Predicted differences in mean annual productivity between mixtures were strongly influenced by soil type and were closely linked to mean annual soil water availability across all soil types. Differences in the CV of productivity were only strongly related to interannual variance in water availability for the lowest water‐holding capacity soil. Our results show that there is considerable scope for mixtures including drought‐tolerant species to enhance certainty in intensive pastoral systems. This provides justification for investing resources in a large‐scale distributed experiment involving many sites under different environmental contexts to confirm these findings.     

Beyond just sea‐level rise: considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change

Due to their position at the land‐sea interface, coastal wetlands are vulnerable to many aspects of climate change. However, climate change vulnerability assessments for coastal wetlands generally focus solely on sea‐level rise without considering the effects of other facets of climate change. Across the globe and in all ecosystems, macroclimatic drivers (e.g., temperature and rainfall regimes) greatly influence ecosystem structure and function. Macroclimatic drivers have been the focus of climate change‐related threat evaluations for terrestrial ecosystems, but largely ignored for coastal wetlands. In some coastal wetlands, changing macroclimatic conditions are expected to result in foundation plant species replacement, which would affect the supply of certain ecosystem goods and services and could affect ecosystem resilience. As examples, we highlight several ecological transition zones where small changes in macroclimatic conditions would result in comparatively large changes in coastal wetland ecosystem structure and function. Our intent in this communication is not to minimize the importance of sea‐level rise. Rather, our overarching aim is to illustrate the need to also consider macroclimatic drivers within vulnerability assessments for coastal wetlands.     

Estimating the uncertainty in annual net ecosystem carbon exchange: spatial variation in turbulent fluxes and sampling errors in eddy-covariance measurements

Above forest canopies, eddy covariance (EC) measurements of mass (CO₂, H₂O vapor) and energy exchange, assumed to represent ecosystem fluxes, are commonly made at one point in the roughness sublayer (RSL). A spatial variability experiment, in which EC measurements were made from six towers within the RSL in a uniform pine plantation, quantified large and dynamic spatial variation in fluxes. The spatial coefficient of variation (CV) of the scalar fluxes decreased with increasing integration time, stabilizing at a minimum that was independent of further lengthening the averaging period (hereafter a ‘stable minimum’). For all three fluxes, the stable minimum (CV=9–11%) was reached at averaging times (τ_p) of 6–7 h during daytime, but higher stable minima (CV=46–158%) were reached at longer τ_p (>12 h) during nighttime. To the extent that decreasing CV of EC fluxes reflects reduction in micrometeorological sampling errors, half of the observed variability at τ_p=30 min is attributed to sampling errors. The remaining half (indicated by the stable minimum CV) is attributed to underlying variability in ecosystem structural properties, as determined by leaf area index, and perhaps associated ecosystem activity attributes. We further assessed the spatial variability estimates in the context of uncertainty in annual net ecosystem exchange (NEE). First, we adjusted annual NEE values obtained at our long‐term observation tower to account for the difference between this tower and the mean of all towers from this experiment; this increased NEE by up to 55 g C m^?2 yr^?1. Second, we combined uncertainty from gap filling and instrument error with uncertainty because of spatial variability, producing an estimate of variability in annual NEE ranging from 79 to 127 g C m^?2 yr^?1. This analysis demonstrated that even in such a uniform pine plantation, in some years spatial variability can contribute ～50% of the uncertainty in annual NEE estimates.     

Predicting maximal aerobic capacity (VO2max) from the critical velocity test in female collegiate rowers

The objective of this study was to examine the relationship between the critical velocity (CV) test and maximal oxygen consumption (VO2max) and develop a regression equation to predict VO2max based on the CV test in female collegiate rowers. Thirty-five female (mean ± SD; age, 19.38 ± 1.3 years; height, 170.27 ± 6.07 cm; body mass, 69.58 ± 0.3 1 kg) collegiate rowers performed 2 incremental VO2max tests to volitional exhaustion on a Concept II Model D rowing ergometer to determine VO2max. After a 72-hour rest period, each rower completed 4 time trials at varying distances for the determination of CV and anaerobic rowing capacity (ARC). A positive correlation was observed between CV and absolute VO2max (r = 0.775, p < 0.001) and ARC and absolute VO2max (r = 0.414, p = 0.040). Based on the significant correlation analysis, a linear regression equation was developed to predict the absolute VO2max from CV and ARC (absolute VO2max = 1.579[CV] + 0.008[ARC] - 3.838; standard error of the estimate [SEE] = 0.192 L·min(-1)). Cross validation analyses were performed using an independent sample of 10 rowers. There was no significant difference between the mean predicted VO2max (3.02 L·min(-1)) and the observed VO2max (3.10 L·min(-1)). The constant error, SEE and validity coefficient (r) were 0.076 L·min(-1), 0.144 L·min(-1), and 0.72, respectively. The total error value was 0.155 L·min(-1). The positive relationship between CV, ARC, and VO2max suggests that the CV test may be a practical alternative to measuring the maximal oxygen uptake in the absence of a metabolic cart. Additional studies are needed to validate the regression equation using a larger sample size and different populations (junior- and senior-level female rowers) and to determine the accuracy of the equation in tracking changes after a training intervention.     

GAM模型和BRT模型在长江口鱼类群落多样性预测中的比较

长江口为西太平洋最大的河口,评估其鱼类群落多样性分布能够为长江口生态系统的修复和管理提供科学依据.本研究基于2012—2014年长江口渔业监测数据,分别使用GAM模型和BRT模型建立各站点水域鱼类群落多样性指数与环境和时空因子之间的关系.结合线性回归方程,采用交叉验证的方式对模型的预测能力和拟合效果进行评价,并绘制了2014年长江口鱼类群落多样性指数和丰富度指数的空间分布图.结果表明: 盐度、pH和叶绿素a对多样性指数贡献最高,pH、溶解氧和叶绿素a是对丰富度指数贡献率最高的环境因子.BRT模型对于多样性指数和丰富度指数的拟合和预测结果均优于GAM模型.空间分布预测显示,相较于GAM模型,BRT模型能够对长江口小面积水域间的鱼类群落多样性作更好的区分,河口外侧水域的鱼类群落多样性明显高于河口内侧水域,而北支水域的多样性高于南支水域.     

Mapping ecosystem functions to the valuation of ecosystem services: implications of species–habitat associations for coastal land-use decisions

Habitats and the ecosystem services they provide are part of the world’s portfolio of natural capital assets. Like many components of this portfolio, it is difficult to assess the full economic value of these services, which tends to over-emphasize the value of extractive activities such as coastal development. Building on recent ecological studies of species–habitat linkages, we use a bioeconomic model to value multiple types of habitats as natural capital, using mangroves, sea grass, and coral reefs as our model system. We show how key ecological variables and processes, including obligate and facultative behaviors map into habitat values and how the valuation of these ecological processes can inform decisions regarding coastal development (habitat clearing). Our stylized modeling framework also provides a clear and concise road map for researchers interested in understanding how to make the link between ecosystem function, ecosystem service, and conservation policy decisions. Our findings also highlight the importance of additional ecological research into how species utilize habitats and that this research is not just important for ecological science, but it can and will influence ecosystem service values that, in turn, will impact coastal land-use decisions. While refining valuation methods is not necessarily going to lead to more rational coastal land-use decisions, it will improve our understanding on the ecological–economic mechanisms that contribute to the value of our natural capital assets. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Multiple stressors,nonlinear effects and the implications of climate change impacts on marine coastal ecosystems

Global climate change will undoubtedly be a pressure on coastal marine ecosystems, affecting not only species distributions and physiology but also ecosystem functioning. In the coastal zone, the environmental variables that may drive ecological responses to climate change include temperature, wave energy, upwelling events and freshwater inputs, and all act and interact at a variety of spatial and temporal scales. To date, we have a poor understanding of how climate‐related environmental changes may affect coastal marine ecosystems or which environmental variables are likely to produce priority effects. Here we use time series data (17 years) of coastal benthic macrofauna to investigate responses to a range of climate‐influenced variables including sea‐surface temperature, southern oscillation indices (SOI, Z4), wind‐wave exposure, freshwater inputs and rainfall. We investigate responses from the abundances of individual species to abundances of functional traits and test whether species that are near the edge of their tolerance to another stressor (in this case sedimentation) may exhibit stronger responses. The responses we observed were all nonlinear and some exhibited thresholds. While temperature was most frequently an important predictor, wave exposure and ENSO‐related variables were also frequently important and most ecological variables responded to interactions between environmental variables. There were also indications that species sensitive to another stressor responded more strongly to weaker climate‐related environmental change at the stressed site than the unstressed site. The observed interactions between climate variables, effects on key species or functional traits, and synergistic effects of additional anthropogenic stressors have important implications for understanding and predicting the ecological consequences of climate change to coastal ecosystems.     

A meta‐analysis of soil salinization effects on nitrogen pools,cycles and fluxes in coastal ecosystems

Salinity intrusion caused by land subsidence resulting from increasing groundwater abstraction, decreasing river sediment loads and increasing sea level because of climate change has caused widespread soil salinization in coastal ecosystems. Soil salinization may greatly alter nitrogen (N) cycling in coastal ecosystems. However, a comprehensive understanding of the effects of soil salinization on ecosystem N pools, cycling processes and fluxes is not available for coastal ecosystems. Therefore, we compiled data from 551 observations from 21 peer‐reviewed papers and conducted a meta‐analysis of experimental soil salinization effects on 19 variables related to N pools, cycling processes and fluxes in coastal ecosystems. Our results showed that the effects of soil salinization varied across different ecosystem types and salinity levels. Soil salinization increased plant N content (18%), soil NH₄⁺ (12%) and soil total N (210%), although it decreased soil NO₃^? (2%) and soil microbial biomass N (74%). Increasing soil salinity stimulated soil N₂O fluxes as well as hydrological NH₄⁺ and NO₂^? fluxes more than threefold, although it decreased the hydrological dissolved organic nitrogen (DON) flux (59%). Soil salinization also increased the net N mineralization by 70%, although salinization effects were not observed on the net nitrification, denitrification and dissimilatory nitrate reduction to ammonium in this meta‐analysis. Overall, this meta‐analysis improves our understanding of the responses of ecosystem N cycling to soil salinization, identifies knowledge gaps and highlights the urgent need for studies on the effects of soil salinization on coastal agro‐ecosystem and microbial N immobilization. Additional increases in knowledge are critical for designing sustainable adaptation measures to the predicted intrusion of salinity intrusion so that the productivity of coastal agro‐ecosystems can be maintained or improved and the N losses and pollution of the natural environment can be minimized.     

Using species‐environmental amplitudes to predict pH values from vegetation

Question: Species optima or indicator values are frequently used to predict environmental variables from species composition. The present study focuses on the question whether predictions can be improved by using species environmental amplitudes instead of single values representing species optima. Location: Semi‐natural, deciduous hardwood forests of northwestern Germany. Methods: Based on a data set of 558 relevés, species responses (presence/absence) to pH were modelled with Huisman‐Olff‐Fresco (HOF) regression models. Species amplitudes were derived from response curves using three different methods. To predict the pH from vegetation, a maximum amplitude overlap method was applied. For comparison, predictions resulting from several established methods, i. e. maximum likelihood/present and absent species, maximum likelihood/present species only, mean weighted averages and mean Ellenberg indicator values were calculated. The predictive success (squared Pearson's r and root mean square error of prediction) was evaluated using an independent data set of 151 relevés. Results: Predictions based upon amplitudes defined by maximum Cohen's x probability threshold yield the best results of all amplitude definitions (R²= 0.75, RMSEP = 0.52). Provided there is an even distribution of the environmental variable, amplitudes defined by predicted probability exceeding prevalence are also suitable (R²= 0.76, RMSEP = 0.55). The prediction success is comparable to maximum likelihood (present species only) and – after rescaling – to mean weighted averages. Predicted values show a good linearity to observed pH values as opposed to a curvilinear relationship of mean Ellenberg indicator values. Transformation or rescaling of the predicted values is not required. Conclusions: Species amplitudes given by a minimum and maximum boundary for each species can be used to efficiently predict environmental variables from species composition. The predictive success is superior to mean Ellenberg indicator values and comparable to mean indicator values based on species weighted averages.     

Patterns of floristic richness in vegetation communities of California: regional scale analysis with multi-temporal NDVI

Aim Applying water‐energy dynamics and heterogeneity theory to explain species richness via remote sensing could allow for the regional characterization and monitoring of vegetation community assemblages and their environment. We assess the relationship of multi‐temporal normalized difference vegetation index (NDVI) to plant species richness in vegetation communities. Location California, USA. Methods Sub‐regions containing species inventories for chaparral, coastal sage scrub, foothill woodland, and yellow pine forest communities were intersected with a vegetation community map and an AVHRR NDVI time series for 1990, 1991, 1992, 1995 and 1996. Principal components analysis reduced the AVHRR data to three variables representing the sum and temporal trajectories of NDVI within each community. A fourth variable representing heterogeneity was tested using the standard deviation of the first component. Quadratic forms of these variables were also tested. Species richness was analysed by stepwise regression. Results Chaparral, coastal sage scrub, and yellow pine forest had the best relationships between species richness and NDVI. Richness of chaparral was related to NDVI heterogeneity and spring greenness (r² varied between 0.26 and 0.62 depending on year of NDVI data). Richness of coastal sage scrub was nonlinearly related to annual NDVI and heterogeneity (r² 0.63–0.81), with peak richness at intermediate values. Foothill woodland richness was related to heterogeneity in a monotonic curvilinear fashion (r² 0.28–0.35). Yellow pine forest richness was negatively related to spring greenness and positively related to heterogeneity (r² 0.40–0.46). Main Conclusions While NDVI's relationship to species richness varied, the selection of NDVI variables was generally consistent across years and indicated that spatial variability in NDVI may reflect important patterns in water‐energy use that affect plant species richness. The principal component axis that should correspond closely with annual mean NPP showed a less prominent role. We conclude that plant species richness for coarse vegetation associations can be characterized and monitored at a regional scale and over long periods of time using relatively coarse resolution NDVI data.     

Interacting Coastal Based Ecosystem Services: Recreation and Water Quality in Puget Sound,WA

Coastal recreation and water quality are major contributors to human well-being in coastal regions. They can also interact, creating opportunities for ecosystem based management, ecological restoration, and water quality improvement that can positively affect people and the environment. Yet the effect of environmental quality on human behavior is often poorly quantified, but commonly assumed in coastal ecosystem service studies. To clarify this effect we investigate a water quality dataset for evidence that environmental condition partially explains variation in recreational visitation, our indicator of human behavior. In Puget Sound, WA, we investigate variation in visitation in both visitation rate and fixed effects (FE) models. The visitation rate model relates the differences in annual recreational visitation among parks to environmental conditions, park characteristics, travel cost, and recreational demand. In our FE model we control for all time-invariant unobserved variables and compare monthly variation at the park level to determine how water quality affects visitation during the summer season. The results of our first model illustrate how visitation relates to various amenities and costs. In the FE analysis, monthly visitation was negatively related to water quality while controlling for monthly visitation trends. This indicates people are responding to changes in water quality, and an improvement would yield an increase in the value of recreation. Together, these results could help in prioritizing water quality improvements, could assist the creation of new parks or the modification of existing recreational infrastructure, and provide quantitative estimates for the expected benefits from potential changes in recreational visitation and water quality improvements. Our results also provide an example of how recreational visitation can be quantified and used in ecosystem service assessments.     

A pseudo-bayesian shrinkage approach to regression with missing covariates

Summary We consider the linear regression of outcome Y on regressors W and Z with some values of W missing, when our main interest is the effect of Z on Y, controlling for W. Three common approaches to regression with missing covariates are (i) complete‐case analysis (CC), which discards the incomplete cases, and (ii) ignorable likelihood methods, which base inference on the likelihood based on the observed data, assuming the missing data are missing at random ( Rubin, 1976b ), and (iii) nonignorable modeling, which posits a joint distribution of the variables and missing data indicators. Another simple practical approach that has not received much theoretical attention is to drop the regressor variables containing missing values from the regression modeling (DV, for drop variables). DV does not lead to bias when either (i) the regression coefficient of W is zero or (ii) W and Z are uncorrelated. We propose a pseudo‐Bayesian approach for regression with missing covariates that compromises between the CC and DV estimates, exploiting information in the incomplete cases when the data support DV assumptions. We illustrate favorable properties of the method by simulation, and apply the proposed method to a liver cancer study. Extension of the method to more than one missing covariate is also discussed.     

Break-through in predictive modelling opens new possibilities for aquatic ecology and management – a review

Due to the complex nature of ecosystems, it has long been argued that process-based dynamic models will never predict well, and numerous studies and critical model tests have also shown this and that simple regression models often predict better for less work. A new generation of dynamic models have, however, been presented that invalidate previous statements about the predictive power of more comprehensive process-oriented dynamic models. These new dynamic models predict important ecosystem variables very well from few and readily accessible driving variables. This paper gives a review of these new models (mass-balance modelling for lakes, rivers and coastal areas and foodweb modelling based on functional groups) and highlights some important reasons for this break-through in modelling in terms of predictive power, wide applicability and practical use. This open new possibilities in aquatic ecology and ecosystem management, e.g., (1) to predict ecosystem effects of pollutants, (2) to estimate changes in the structure of aquatic foodwebs related to future climate changes, (3) to predict consequences of fish kill catastrophes and biomanipulations and (4) to develop new approaches to set fish quota to complement the methods used today where fish quotas are set from fish catch statistics, and not from the amount of food available for fish and for the prey of the fish, i.e., from the presuppositions given by the aquatic foodweb.     

Effect of Screening Out Implausible Energy Intake Reports on Relationships between Diet and BMI

Objective: We present an updated method for identifying physiologically implausible dietary reports by comparing reported energy intake (rEI) with predicted energy requirements (pER), and we examine the impact of excluding these reports. Research Methods and Procedures: Adult data from the Continuing Survey of Food Intakes by Individuals 1994 to 1996 were used. pER was calculated from the dietary reference intake equations. Within‐subject variations and errors in rEI [coefficient of variation (CV) ～ 23%] over 2 days (d), pER (CV ～ 11%), and measured total energy expenditure (mTEE; doubly labeled water, CV ～ 8.2%) were propagated, where ±1 SD = . Thus, a report was identified as implausible if rEI was not within 78% to 122% of pER. Multiple cut‐offs between ±1 and ±2 SD were tested. Results: %rEI/pER = 81% in the total sample (n = 6499) and progressively increased to 95% in the ±1 SD sample (n = 2685). The ±1 to 1.4 SD samples yielded rEI‐weight associations closest to the theoretical relationship (mTEE to weight). Weak or spurious diet—BMI associations were present in the total sample; ±1 to 1.4 SD samples showed the strongest set of associations and provided the maximum n while maintaining biological plausibility. Discussion: Our methodology can be applied to different data sets to evaluate the impact of implausible rEIs on health outcomes. Implausible rEIs reduce the overall validity of a sample, and not excluding them may lead to inappropriate conclusions about potential dietary causes of health outcomes such as obesity.     

Measurement errors and regression to the mean cannot explain bias in average Ellenberg indicator values

Smart & Scott (2004, this is sue) criticized our paper (Wamelink et al. 2002) about the bias in average Ellenberg indicator values. Their main criticism concerns the method we used, regression analysis. They state the bias can be mimicked by the construction of an artificial data set and that regression analysis is not a suited tool to investigate underlying phenomena. Moreover they claim that the present bias is caused by the distribution of Ellenberg indicator values between syntaxa, instead of a bias in average Ellenberg indicator values per species. We show that their criticism of the use of regression analysis does not hold. We selected average Ellenberg values per vegetation group for several pH classes and applied an F‐test to determine whether or not the vegetation groups within each pH class differed significantly from each other. This was the case for all tested classes (P < 0.001). Moreover we simulated an artificial data set, of which the F‐test for varying measurement error could not explain the magnitude of the F‐value we found earlier. This indicates that the bias we found in average Ellenberg indicator values cannot be explained by measurement errors or by regression to the mean. In the end, Smart & Scott, as we did, come to the conclusion that there is a bias present and that separate regression lines per vegetation type are necessary, but the debate remains open on whether or not this is caused by the bias in Ellenberg indicator values per species.     

广西北部湾沿岸地区生态系统服务价值变化及其驱动力

掌握生态系统结构及其功能的时空变化规律是开展科学生态系统管理的重要前提,如何衡量人类在满足自我需求的同时对自然资源和生态系统的改变程度是当前研究值得关注的一个问题。伴随北部湾经济区的崛起,区域经济发展与生态保护的矛盾日益凸显,海水倒灌、植被退化和土地沙化等现象加剧,生态系统服务可持续供给受到严重威胁。以时序遥感数据为基础,分析北部湾沿岸地区生态系统时空演变,评估生态系统服务价值时空变化规律及其驱动因素,为区域生态系统管理提供科学基础。研究显示:1999—2014年,城市点状、离散扩张使城市生态系统面积显著增加,破碎化程度加剧;环境恶化给湿地生态系统带来毁灭性的破坏,红树林不断消失,破碎度增加;林地和果园构成的森林生态系统面积有所增加,而耕地面积急剧缩减。生态系统构成中,森林生态系统服务价值最高,约占研究区总价值的50%,且呈增长趋势;生态系统服务构成中,除食物生产、气体调节和维持养分循环价值有所减少外,其他类型生态系统服务价值均呈增长趋势。驱动力分析表明,综合城镇化率是影响北部湾沿岸地区生态系统服务价值变化的重要驱动因素,说明区域生态系统服务与社会经济发展极为相关,合理调控经济结构可有效提升区域生态系统服务价值。     

Integrating ecophysiology and forest landscape models to improve projections of drought effects under climate change

Fundamental drivers of ecosystem processes such as temperature and precipitation are rapidly changing and creating novel environmental conditions. Forest landscape models (FLM) are used by managers and policy‐makers to make projections of future ecosystem dynamics under alternative management or policy options, but the links between the fundamental drivers and projected responses are weak and indirect, limiting their reliability for projecting the impacts of climate change. We developed and tested a relatively mechanistic method to simulate the effects of changing precipitation on species competition within the LANDIS‐II FLM. Using data from a field precipitation manipulation experiment in a piñon pine (Pinus edulis) and juniper (Juniperus monosperma) ecosystem in New Mexico (USA), we calibrated our model to measurements from ambient control plots and tested predictions under the drought and irrigation treatments against empirical measurements. The model successfully predicted behavior of physiological variables under the treatments. Discrepancies between model output and empirical data occurred when the monthly time step of the model failed to capture the short‐term dynamics of the ecosystem as recorded by instantaneous field measurements. We applied the model to heuristically assess the effect of alternative climate scenarios on the piñon–juniper ecosystem and found that warmer and drier climate reduced productivity and increased the risk of drought‐induced mortality, especially for piñon. We concluded that the direct links between fundamental drivers and growth rates in our model hold great promise to improve our understanding of ecosystem processes under climate change and improve management decisions because of its greater reliance on first principles.