Modeling leaf dispersal in mixed hardwood forests using a ballistic approach

In mixed-species stands, modeling leaf litter dispersal is important to predict the physical and chemical characteristics of the forest floor, which plays a major role in nutrient cycling and in plant population dynamics. In this study, a spatially explicit model of leaf litterfall was developed and compared with two other models. These three models were calibrated for a mixed forest of oak and beech using litterfall data from mapped forest plots. All models assumed that an allometric equation described individual leaf litter production, but they strongly differed in the modeling of the probability density of leaf shedding with distance from source trees. Two models used a negative exponential function to account for leaf dispersal with distance, and this function was allowed to vary according to wind direction in one of them. In contrast, our approach was based on a simple ballistic equation considering release height, wind speed, wind direction, and leaf fall velocity; the distributions of wind speeds and wind directions were modeled according to a Weibull and a Von Mises distribution, respectively. Using an independent validation data set, all three models provided predictions well correlated to measurements (r > 0.83); however, the two models with a direction-dependent component were slightly more accurate. In addition, parameter estimates of the ballistic model were in close agreement with a foliar litter production equation derived from the literature for beech and with wind characteristics measured during leaf litterfall for both species. Because of its mechanistic background, such a spatially explicit model might be incorporated as a litterfall module in larger models (nutrient cycling, plant population dynamics) or used to determine the manner in which patch size in mixed-species stands influences litter mixture.     

Controls over foliar N:P ratios in tropical rain forests

Correlations between foliar nutrient concentrations and soil nutrient availability have been found in multiple ecosystems. These relationships have led to the use of foliar nutrients as an index of nutrient status and to the prediction of broadscale patterns in ecosystem processes. More recently, a growing interest in ecological stoichiometry has fueled multiple analyses of foliar nitrogen:phosphorus (N:P) ratios within and across ecosystems. These studies have observed that N:P values are generally elevated in tropical forests when compared to higher latitude ecosystems, adding weight to a common belief that tropical forests are generally N rich and P poor. However, while these broad generalizations may have merit, their simplicity masks the enormous environmental heterogeneity that exists within the tropics; such variation includes large ranges in soil fertility and climate, as well as the highest plant species diversity of any biome. Here we present original data on foliar N and P concentrations from 150 mature canopy tree species in Costa Rica and Brazil, and combine those data with a comprehensive new literature synthesis to explore the major sources of variation in foliar N:P values within the tropics. We found no relationship between N:P ratios and either latitude or mean annual precipitation within the tropics alone. There is, however, evidence of seasonal controls; in our Costa Rica sites, foliar N:P values differed by 25% between wet and dry seasons. The N:P ratios do vary with soil P availability and/or soil order, but there is substantial overlap across coarse divisions in soil type, and perhaps the most striking feature of the data set is variation at the species level. Taken as a whole, our results imply that the dominant influence on foliar N:P ratios in the tropics is species variability and that, unlike marine systems and perhaps many other terrestrial biomes, the N:P stoichiometry of tropical forests is not well constrained. Thus any use of N:P ratios in the tropics to infer larger-scale ecosystem processes must comprehensively account for the diversity of any given site and recognize the broad range in nutrient requirements, even at the local scale.     

No evidence that chronic nitrogen additions increase photosynthesis in mature sugar maple forests

Atmospheric nitrogen (N) deposition can increase forest growth. Because N deposition commonly increases foliar N concentrations, it is thought that this increase in forest growth is a consequence of enhanced leaf-level photosynthesis. However, tests of this mechanism have been infrequent, and increases in photosynthesis have not been consistently observed in mature forests subject to chronic N deposition. In four mature northern hardwood forests in the north-central United States, chronic N additions (30 kg N ha(-1) yr(-1) as NaNO3 for 14 years) have increased aboveground growth but have not affected canopy leaf biomass or leaf area index. In order to understand the mechanism behind the increases in growth, we hypothesized that the NO3(-) additions increased foliar N concentrations and leaf-level photosynthesis in the dominant species in these forests (sugar maple, Acer saccharum). The NO3(-) additions significantly increased foliar N. However, there was no significant difference between the ambient and +NO3(-) treatments in two seasons (2006-2007) of instantaneous measurements of photosynthesis from either canopy towers or excised branches. In measurements on excised branches, photosynthetic nitrogen use efficiency (micromol CO2 s(-1) g(-1) N) was significantly decreased (-13%) by NO3(-) additions. Furthermore, we found no consistent NO3(-) effect across all sites in either current foliage or leaf litter collected annually throughout the study (1993-2007) and analyzed for delta 13C and delta 18O, isotopes that can be used together to integrate changes in photosynthesis over time. We observed a small but significant NO3(-) effect on the average area and mass of individual leaves from the excised branches, but these differences varied by site and were countered by changes in leaf number. These photosynthesis and leaf area data together suggest that NO3(-) additions have not stimulated photosynthesis. There is no evidence that nutrient deficiencies have developed at these sites, so unlike other studies of photosynthesis in N-saturated forests, we cannot attribute the lack of a stimulation of photosynthesis to nutrient limitations. Rather than increases in C assimilation, the observed increases in aboveground growth at our study sites are more likely due to shifts in C allocation.     

紫花芒果NPKCaMg叶片诊断的DRIS标准初步研究

按诊断与推荐综合系统（DRIS）标准建立的常规方法.通过比较低产和高产组叶片N、P、K、Ca、Mg养分含量以及两元素间的养分比和养分积,初步筛选出N／P、N／K、N／Ca、N／Mg、K／P、Ca/P、P／Mg、K／Ca.K／Mg和Ca／Mg作为DRIS参项,并初步提出紫花芒果DRIS诊断标准.     

氮沉降下西南亚高山针叶林生长的养分限制特征

长期氮(N)沉降及其诱导的N、磷(P)养分平衡性对森林生长与生产力的生态反馈效应已成为当前森林生态学研究的前沿与热点,但目前大多研究主要基于已有文献数据整合分析,而缺乏野外原位系统性研究与试验证据.以西南亚高山两种典型人工针叶林——云杉(Picea asperata)林和华山松(Pinus armandii)林为对象,...     

Regression equations for estimating throughfall nutrient fluxes using wet deposition data and their applicability for simulating the soil acid–base status using the dynamic forest soil–atmosphere model SAFE

Throughfall may contribute large amounts of nutrients to forest soils via the leaching of accumulated dry particulates on the canopy, and by altering incoming precipitation, it may have some control on the acid–base status of the soil. Unfortunately, information about throughfall in forests is sparse and thus, scientists must deal with this gap in knowledge before conducting regional applications of dynamic soil acidification models. The first objective of this paper was to test the possibility of developing regression equations that could allow modellers to estimate throughfall nutrient fluxes using wet deposition nutrient fluxes as input data. The second objective was to test the relative importance of this simplification on regional applications of the dynamic soil–atmosphere model Soil Acidification in Forested Ecosystems (SAFE) using one published application of this model as the base case. Annual throughfall nutrient fluxes were estimated successfully from annual wet deposition fluxes for individual ions. The success of these relationships were however inversely proportional to the intensity at which an ion was involved in exchange reactions: models generally performed better with more conservative ions. The simulation of the soil acid–base status with SAFE suggested that it was appropriate to use the throughfall estimates yielded using the regression equations. Also, testing of the SAFE output using different regression equations in throughfall showed that, in the case of base cations, the key for modelling the soil acid–base status was to produce accurate throughfall estimates of Ca and Mg, and that K had marginal effects. However, a small bias in solution pH was introduced as the balance between alkalinity and acidity in the different categories of deposition appeared to be diverging from the base case (measured) values. The use of our approach at other sites may indicate if there is a systematic bias or not in the regressions. Yet, results suggest that the regression equations are appropriate for the purpose of modelling the soil acid–base status at the scale of the landscape because it assures that the same set of assumptions in throughfall are used for each application.     

Nutrient criteria for lakes,ponds, and reservoirs: A Bayesian TREED model approach

We develop regional-scale eutrophication models for lakes, ponds, and reservoirs to investigate the link between nutrients and chlorophyll-a. The Bayesian TREED (BTREED) model approach allows association of multiple environmental stressors with biological responses, and quantification of uncertainty sources in the empirical water quality model. Nutrient data for lakes, ponds, and reservoirs across the United States were obtained from the Environmental Protection Agency (EPA) National Nutrient Criteria Database. The nutrient data consist of measurements for both stressor variables (such as total nitrogen and total phosphorus), and response variables (such as chlorophyll-a), used in the BTREED model. Markov chain Monte Carlo (McMC) posterior exploration guides a stochastic search through a rich suite of candidate trees toward models that better fit the data. The Bayes factor provides a goodness of fit criterion for comparison of resultant models. We randomly split the data into training and test sets; the training data were used in model estimation, and the test data were used to evaluate out-of-sample predictive performance of the model. An average relative efficiency of 1.02 between the training and test data for the four highest log-likelihood models suggests good out-of-sample predictive performance. Reduced model uncertainty relative to over-parameterized alternative models makes the BTREED models useful for nutrient criteria development, providing the link between nutrient stressors and meaningful eutrophication response.     

Gastropod herbivory in response to elevated CO2 and N addition impacts plant community composition

In this study, the influence of elevated carbon dioxide (CO2) and nitrogen (N) deposition on gastropod herbivory was investigated for six annual species in a California annual grassland community. These experimentally simulated global changes increased availability of important resources for plant growth, leading to the hypothesis that species with the most positive growth and foliar nutrient responses would experience the greatest increase in herbivory. Counter to the expectations, shifts in tissue N and growth rates caused by N deposition did not predict shifts in herbivore consumption rates. N deposition increased seedling N concentrations and growth rates but did not increase herbivore consumption overall, or for any individual species. Elevated CO2 did not influence growth rates nor have a statistically significant influence on seedling N concentrations. Elevated CO2 at ambient N levels caused a decline in the number of seedlings consumed, but the interaction between CO2 and N addition differed among species. The results of this study indicate that shifting patterns of herbivory will likely influence species composition as environmental conditions change in the future; however, a simple trade-off between shifting growth rates and palatability is not evident.     

Terpenes and phenolics in response to nitrogen fertilization: A test of the carbon/nutrient balance hypothesis

Nitrogen fertilization resulted in a linear increase in the growth ofAbies grandis seedlings, but linear decrease in foliage concentrations of phenolic compounds. These data are consistent with the inverse relationship between growth and production of carbonbased secondary chemicals predicted by the carbon/nutrient balance (CNB) hypothesis. However, in contrast to predictions of the CNB hypothesis, nitrogen fertilization had no effect on foliage terpene concentrations. The results suggest that not all carbon-based chemicals respond in the same manner to environmental variation, and that the carbon/nutrient balance hypothesis does not adequately explain all patterns of environmentally-induced variation in secondary metabolism.     

不同绿地土壤磷水平对麦冬叶养分的影响

对合肥环城公园不同地段绿地土壤及地被植物麦冬叶片的氮磷含量进行了调查分析。结果表明土壤全氮含量平均为1.36 g/kg、全磷为1451.89 mg/kg、有效磷为84.57 mg/kg;麦冬叶的氮磷含量平均值分别为17.25 g/kg和2.83 g/kg,氮磷比为6.33。叶片氮磷比与土壤全磷、有效磷含量呈显著负相关,而与土壤氮含量没有显著相关性。基于叶片氮磷计量关系,调查区植物生长明显受到氮限制。     

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.     

Investigating the complex relationship between in situ Southern Ocean pCO_{2} and its ocean physics and biogeochemical drivers using a nonparametric regression approach

The objective in this paper is to investigate the use of a non-parametric approach to model the relationship between oceanic carbon dioxide \((pCO_2)\) and a range of ocean physics and biogeochemical in situ variables in the Southern Ocean, which influence its in situ variability. The need for this stems from the need to obtain reliable estimates of carbon dioxide concentrations in the Southern Ocean which plays an important role in the global carbon flux cycle. The main challenge involved in this objective is the spatial limitation and seasonal bias of the in situ data. Moreover, studies have also reported that the relationship between \(pCO_2\) and its drivers is complex. As such, in this paper, we use the non-parametric kernel regression approach since it is able to accurately capture the complex relationships between the response and predictor variables. In this analysis we use the in situ data obtained from the SANAE49 return leg journey between Antarctic to Cape Town. To the best of our knowledge, this is the first time this data set has been subjected to such analysis. The model variants were developed on a training data subset, and the ‘goodness’ of the models were assessed on an “unseen” test data subset. Results indicate that the nonparametric approach consistently captures the relationship more accurately in terms of mean square error, root mean square error and mean absolute error, over a standard parametric approach (multiple linear regression). These results provide a platform for using the developed nonparametric regression model based on in situ measurements to predict \(pCO_2\) for a larger spatial region in the Southern Ocean based on satellite biogeochemical measurements of predictor variables, given that satellites do not measure \(pCO_2\) .     

Nitrogen export from an agriculture watershed in the Taihu Lake area, China

Temporal changes in nitrogen concentrations and stream discharge, as well as sediment and nitrogen losses from erosion plots with different land uses, were studied in an agricultural watershed in the Taihu Lake area in eastern China. The highest overland runoff loads and nitrogen losses were measured under the upland at a convergent footslope. Much higher runoff, sediment and nitrogen losses were observed under upland cropping and vegetable fields than that under chestnut orchard and bamboo forest. Sediment associated nitrogen losses accounted for 8-43.5% of total nitrogen export via overland runoff. N lost in dissolved inorganic nitrogen forms (NO(3-)-N + NH4+-N) accounted for less than 50% of total water associated nitrogen export. Agricultural practices and weather-driven fluctuation in discharge were main reasons for the temporal variations in nutrient losses via stream discharge. Significant correlation between the total nitrogen concentration and stream discharge load was observed. Simple regression models could give satisfactory results for prediction of the total nitrogen concentrations in stream water and can be used for better quantifying nitrogen losses from arable land. Nitrogen losses from the studied watershed via stream discharge during rice season in the year 2002 were estimated to be 10.5 kg N/ha using these simple models.     

Physiological and developmental effects of O3 on cottonwood growth in urban and rural sites.

Previously we found that cloned cottonwood saplings (Populus deltoides) grew twice as large in New York, New York, USA, compared to surrounding rural environments and that soils, temperature, CO2, nutrient deposition, and microclimatic variables could not account for the greater urban plant biomass. Correlations between final season biomass and cumulative O3 exposures, combined with twofold growth reductions in an open-top chamber experiment provided strong evidence that higher cumulative O3 exposures in rural sites reduced growth in the country. Here, we assess the field gas exchange, growth and development, and allocation responses underlying the observed growth differences and compare them with isolated O3 responses documented in the open-top chamber experiment. Cottonwoods showed no visible foliar injury, reduced photosynthesis of recently expanded foliage, early leaf senescence, protective reduction in stomatal conductance, or compensatory allocation to shoot relative to root biomass for either the chamber or field experiment. Instead, O3-impacted chamber plants had significantly higher conductance and reduced photosynthesis of older foliage that led to reduced leaf area production and a twofold biomass reduction in the absence of visible injury. Rural-grown field plants showed the same pattern of significantly higher conductance in the absence of concomitant increases in photosynthesis that was indicative of a loss of stomatal control. Incremental changes in foliar production were also significantly inversely related to fluctuations in ambient O3 exposures. The similarity in biomass, gas exchange, phenological, and allocation responses between chamber and field experiments indicate that mechanisms accounting for reduced growth at rural sites were consistent with those in the open-top chamber O3 experiment. This study shows the limitation of visible symptoms as a sole diagnostic factor for documenting detrimental O3 impacts and points toward a new approach to show O3 impacts when visible injury is not present. Namely, O3-impacted vegetation showed an unusual inverse relationship of increased conductance with lower photosynthesis of older foliage that was indicative of a loss of stomatal control. This increased stomatal conductance of O3-impacted vegetation accentuates pollutant flux into affected foliage and has important implications for system water balance during warm, dry portions of the growing season when O3 concentrations are highest.     

Inferring extinction of mammals from sighting records, threats, and biological traits

For species with five or more sightings, quantitative techniques exist to test whether a species is extinct on the basis of distribution of sightings. However, 70% of purportedly extinct mammals are known from fewer than five sightings, and such models do not include some important indicators of the likelihood of extinction such as threats, biological traits, search effort, and demography. Previously, we developed a quantitative method that we based on species' traits in which we used Cox proportional hazards regression to calculate the probability of rediscovery of species regarded as extinct. Here, we used two versions of the Cox regression model to determine the probability of extinction in purportedly extinct mammals and compared the results of these two models with those of stationary Poisson, nonparametric, and Weibull sighting-distribution models. For mammals with five or more sightings, the stationary Poisson model categorized all but two critically endangered (flagged as possibly extinct) species in our data set as extinct, and results with this model were consistent with current categories of the International Union for the Conservation of Nature. The scores of probability of rediscovery for individual species in one version of our Cox regression model were correlated with scores assigned by the stationary Poisson model. Thus, we used this Cox regression model to determine the probability of extinction of mammals with sparse records. On the basis of the Cox regression model, the most likely mammals to be rediscovered were the Montane monkey-faced bat (Pteralopex pulchra), Armenian myotis (Myotis hajastanicus), Alcorn's pocket gopher (Pappogeomys alcorni), and Wimmer's shrew (Crocidura wimmeri). The Cox model categorized two species that have recently disappeared as extinct: the baiji (Lipotes vexillifer) and the Christmas Island pipistrelle (Pipistrellus murrayi). Our new method can be used to test whether species with few records or recent last-sighting dates are likely to be extinct.     

Persistence of rock-derived nutrients in the wet tropical forests of La Selva, Costa Rica

We used strontium isotopes and analysis of foliar and soil nutrients to test whether erosion can rejuvenate the supply of rock-derived nutrients in the lowland tropical rain forest of La Selva, Costa Rica. We expected that these nutrients would be depleted from soils on stable surfaces, a result of over one million years of weathering in situ. In fact, trees and palms in all landscape positions derive a relatively high percentage (> or =40%) of their strontium from bedrock, rather than atmospheric, sources. The fraction that is rock-derived increases on slopes, but with no detectable effect on plant macronutrient concentrations. These results differ from those in a similar ecosystem on Kauai, Hawaii, where plants on uneroded surfaces derive almost all of their foliar Sr from atmospheric, rather than bedrock, sources. The results from La Selva challenge the assumption that tropical Oxisols in general have low nutrient inputs from bedrock, and support the hypothesis that erosion can increase the supply of these nutrients in lower landscape positions.     

Nitrogen Export from an Agriculture Watershed in the Taihu Lake Area, China

Temporal changes in nitrogen concentrations and stream discharge, as well as sediment and nitrogen losses from erosion plots with different land uses, were studied in an agricultural watershed in the Taihu Lake area in eastern China. The highest overland runoff loads and nitrogen losses were measured under the upland at a convergent footslope. Much higher runoff, sediment and nitrogen losses were observed under upland cropping and vegetable fields than that under chestnut orchard and bamboo forest. Sediment associated nitrogen losses accounted for 8–43.5% of total nitrogen export via overland runoff. N lost in dissolved inorganic nitrogen forms (NO ₃ ^– -N + NH ₄ ⁺ -N) accounted for less than 50% of total water associated nitrogen export. Agricultural practices and weather-driven fluctuation in discharge were main reasons for the temporal variations in nutrient losses via stream discharge. Significant correlation between the total nitrogen concentration and stream discharge load was observed. Simple regression models could give satisfactory results for prediction of the total nitrogen concentrations in stream water and can be used for better quantifying nitrogen losses from arable land. Nitrogen losses from the studied watershed via stream discharge during rice season in the year 2002 were estimated to be 10.5 kg N/ha using these simple models.     

Relevant variables to predict macrophyte communities in running waters

In both predictive theoretical and empirical models for aquatic plant communities in running waters, the development and competition are many times explained in terms of nutrients. Minerals necessary for growth are generally not assumed to be limiting, although they influence the important pH-value. At the same time it is known that factors such as oxygen-concentration, solar energy, salinity, dimension of the system and soil characteristics (including river sediments) influence the development of the community, and should be considered in modelling. Effects of water quantity and water quality on macrophytes are reviewed. These conditions are caused by processes in the landscape, characterised by a set of nested variables which explain the distribution of macrophyte species and communities. Relevant variables are described and grouped on three scales: regional, local and site conditions. Case studies with direct and indirect gradient analysis are presented. Statistical tests (stepwise regression with forward selection) reveal that each species distribution is explained by a characteristic set of relevant variables, ranging from soil type and dimension of the system, to nutrient and salinity concentration.     

外源稀土La在沉积物中的形态及其生物可利用性

将沉积物中的外源稀土La分为可溶解态、酸可提取态、有机质及硫化物结合态,研究了小球藻(Chlorella Vulgaris Beijerinck)对这三种形态La的生物可利用性.研究结果表明:这三种形态的La在沉积物中的含量与其在藻中的含量有很好的相关关系.其中可溶解态La的生物可利用性最大,这说明,沉积物中La的释放对其生物可利用性至关重要.     

Modeling and forecasting daily maximum hourly ozone concentrations using the RegAR model with skewed and heavy-tailed innovations

This paper considers the modeling and forecasting of daily maximum hourly ozone concentrations in Laranjeiras, Serra, Brazil, through dynamic regression models. In order to take into account the natural skewness and heavy-tailness of the data, a linear regression model with autoregressive errors and innovations following a member of the family of scale mixture of skew-normal distributions was considered. Pollutants and meteorological variables were considered as predictors, along with some deterministic factors, namely week-days and seasons. The Oceanic Niño Index was also considered as a predictor. The estimated model was able to explain satisfactorily well the correlation structure of the ozone time series. An out-of-sample forecast study was also performed. The skew-normal and skew-t models displayed quite competitive point forecasts compared to the similar model with gaussian innovations. On the other hand, in terms of forecast intervals, the skewed models presented much better performance with more accurate prediction intervals. These findings were empirically corroborated by a forecast Monte Carlo experiment.