Modelling spatial distribution of the Croatian marine benthic habitats

Within the framework of the 3-year project “Mapping the habitats of the Republic of Croatia” the marine benthic habitats of the entire Croatian maritory were mapped. The supralittoral and the mediolittoral were mapped as a function of the coastal lithology and the presumed levels of human impact (both in scale of 1:100,000). The infralittoral was mapped on the basis of spatial modelling (using neural networks as a modelling tool, data about habitats collected by fieldwork as the independent variable for training and testing the model, and the digital bathymetrical model, the distance from coast, the second spectral channel of Landsat ETM+ satellite image and the sea bottom sea temperature, salinity and current magnitude, as dependent variables). The circalittoral and the bathyal were mapped by overlapping and reinterpretation of the existing spatial databases (bathymetry and lithology) within the framework of the raster-GIS.     

Predicting long-term development of abandoned subalpine conifer forests in the Swiss National Park

In the past 35 years, various kinds of dynamic models have been used to study vegetation development during primary or secondary succession. Typically, one specific model or models with the same conceptual background were employed. It remains largely unknown to what extent such model-based findings, e.g., on the speed of succession, depend on the specific model approach.To address this issue, we estimated the time elapsing during secondary succession in subalpine conifer forests of the Swiss National Park using three models of different conceptual background: (i) a forest gap model, (ii) a Markov chain model, and (iii) a minimum spanning tree model.Starting from a 95- to 125-year-old mountain pine (Pinus montana Miller) forest, all three models predicted a similar successional development. Even though the forest gap model and the Markov chain model are based on totally different approaches and were calibrated using different data sets, they both forecasted that it would take 500–550 years to reach a late-successional forest stage. The minimum spanning tree model, which only reveals a certain number of time steps yielding a minimum time estimate, showed a development of tree density (stems/ha) that was similar to the results of the forest gap model, but a strict quantitative comparison is not feasible.Our study shows that modeling forest development using three different approaches is quite powerful to obtain a robust estimate of the speed of forest succession. In our case, this estimate is higher than what has been suggested in previous studies that investigated secondary forest succession. The use of several approaches allows for a more comprehensive analysis in terms of variables covered (e.g., relative forest cover in the Markov approach vs. stand-scale species composition in the forest gap model). We recommend that in studies focusing on the speed of succession, several models should be employed simultaneously to identify inconsistencies in our knowledge and to increase confidence in the results.     

Effect of prior disturbances on the extent and severity of wildfire in Colorado subalpine forests

Disturbances are important in creating spatial heterogeneity of vegetation patterns that in turn may affect the spread and severity of subsequent disturbances. Between 1997 and 2002 extensive areas of subalpine forests in northwestern Colorado were affected by a blowdown of trees, bark beetle outbreaks, and salvage logging. Some of these stands were also affected by severe fires in the late 19th century. During a severe drought in 2002, fires affected extensive areas of these subalpine forests. We evaluated and modeled the extent and severity of the 2002 fires in relation to these disturbances that occurred over the five years prior to the fires and in relation to late 19th century stand-replacing fires. Occurrence of disturbances prior to 2002 was reconstructed using a combination of tree-ring methods, aerial photograph interpretation, field surveys, and geographic information systems (GIS). The extent and severity of the 2002 fires were based on the normalized difference burn ratio (NDBR) derived from satellite imagery. GIS and classification trees were used to analyze the effects of prefire conditions on the 2002 fires. Previous disturbance history had a significant influence on the severity of the 2002 fires. Stands that were severely blown down (> 66% trees down) in 1997 burned more severely than other stands, and young (approximately 120 year old) postfire stands burned less severely than older stands. In contrast, prefire disturbances were poor predictors of fire extent, except that young (approximately 120 years old) postfire stands were less extensively burned than older stands. Salvage logging and bark beetle outbreaks that followed the 1997 blowdown (within the blowdown as well as in adjacent forest that was not blown down) did not appear to affect fire extent or severity. Conclusions regarding the influence of the beetle outbreaks on fire extent and severity are limited, however, by spatial and temporal limitations associated with aerial detection surveys of beetle activity. Thus, fire extent in these forests is largely independent of prefire disturbance history and vegetation conditions. In contrast, fire severity, even during extreme fire weather and in conjunction with a multiyear drought, is influenced by prefire stand conditions, including the history of previous disturbances.     

青藏高原东北边缘桦木林木本植物种间联结

青藏高原东北边缘地区位于我国东部季风区、西北干旱区与青藏高原高寒区的交汇地带,该区既兼具3大自然区的特点,又具有边缘地带气候波动不稳定性和自然生态环境的脆弱性,是全球变化重点监测的地区之一。研究青藏高原东北边缘亚高山桦木林的种间联结,不仅有助于更深刻地认识该地区桦木群落的结构、功能和演替,对于维持该地区群落稳定性以及生态恢复具有重要意义。本文选取青藏高原东北边缘桦木林中20个优势种,采用方差比率法(VR)检验多物种间的联结性,2×2列联表分析共同出现百分率(JI)、点相关系数(Ф)、Dice指数(DI)来表示种间联结程度,用重要值的Pearson相关和Spearman秩相关来检验相伴出现的机率等方法分析其种间关联性。结果表明:群落总体联结VR值为1.094,说明20个种群间表现出净正关联,其统计量W值为10.94说明20个种群之间在总体上的正关联不显著;大多数种对的JI值、DI值在0.4以下,说明种对之间联结程度不显著,Φ值大部分处于[-0.2, 0.2]值域内,说明种对之间的正或负的联结性较弱;而Spearman秩相关所测得的正负关联比要大于Pearson相关所测得的,且Spearman秩相关要优于Pearson相关。从以上结果可以看出,青藏高原东北边缘三种桦树天然次生林处于不稳定的状态,即演替的中期阶段,其逐渐会被地带性植被所取代。     

Modelling the distribution of plant species using the autologistic regression model

For modeling the distribution of plant species in terms of climate covariates, we consider an autologistic regression model for spatial binary data on a regularly spaced lattice. This model belongs to the class of autologistic models introduced by Besag (1974). Three estimation methods, the coding method, maximum pseudolikelihood method and Markov chain Monte Carlo method are studied and comparedvia simulation and real data examples. As examples, we use the proposed methodology to model the distributions of two plant species in the state of Florida.     

Modelling asymmetrically distributed circular data using the wrapped skew-normal distribution

We consider problems of inference for the wrapped skew-normal distribution on the circle. A centered parametrization of the distribution is introduced, and simulation used to compare the performance of method of moments and maximum likelihood estimation for its parameters. Maximum likelihood estimation is shown, in general, to be superior. The operating characteristics of two moment based tests, for wrapped normal and wrapped half-normal parent populations, respectively, are also explored. The former test is easy to apply, maintains the nominal significance level well and is generally highly powerful. The latter test does not hold the nominal significance level so well, although it is very powerful against negatively skew alternatives. Likelihood based tests for the two distributions are also discussed. A real data set from the ornithological literature is used to illustrate the application of the developed methodology and its extension to finite mixture modelling. Received: September 2003/ Revised: April 2005     

川西亚高山针叶林人工恢复过程的土壤性性质变化

研究川西岷江上游高山针叶林区不同年龄阶段的人工云杉林地凋落物及其养分贮量和土壤养分及主要理化性质的变化趋势,结果表明：（1）人工云杉林的凋落物及其氮、磷、钾贮量、以60年代抚育成熟林最高,40年代抚育成熟林大幅度下降,分别下降34．1％及49．8％,70．5％,46．7％;（2）人工云杉林地表土的有机质、全氮、全磷随林龄的增加而降低,据典型土壤剖面资料,40年代比60年代抚育林土壤分别降低72．4％,78．6％,42．2％;（3）相应于土壤有机质的变化,与60年代人工云杉成熟林相比,40年代成熟林土壤的自然含水量、总孔工、保肥力（CEC）和交换性盐某养分等均明显降低,表现出肥力退化的趋势,因此,当人工云杉林达到成熟林后,后采取诸如适当间伐等措施,以改善林地生态条件,避免土壤肥力退化,图4表6参11。     

Modelling spatial distribution of hard bottom benthic communities and their functional response to environmental parameters

In this study we analyzed and modelled spatial distribution of hard bottom benthic communities in the Lagoon of Venice, and used the model to derive functional response of these communities to changing environmental conditions.     

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,...     

A universal approach to estimate biomass and carbon stock in tropical forests using generic allometric models

Allometric equations allow aboveground tree biomass and carbon stock to be estimated from tree size. The allometric scaling theory suggests the existence of a universal power-law relationship between tree biomass and tree diameter with a fixed scaling exponent close to 8/3. In addition, generic empirical models, like Chave's or Brown's models, have been proposed for tropical forests in America and Asia. These generic models have been used to estimate forest biomass and carbon worldwide. However, tree allometry depends on environmental and genetic factors that vary from region to region. Consequently, theoretical models that include too few ecological explicative variables or empirical generic models that have been calibrated at particular sites are unlikely to yield accurate tree biomass estimates at other sites. In this study, we based our analysis on a destructive sample of 481 trees in Madagascar spiny dry and moist forests characterized by a high rate of endemism (> 95%). We show that, among the available generic allometric models, Chave's model including diameter, height, and wood specific gravity as explicative variables for a particular forest type (dry, moist, or wet tropical forest) was the only one that gave accurate tree biomass estimates for Madagascar (R2 > 83%, bias < 6%), with estimates comparable to those obtained with regional allometric models. When biomass allometric models are not available for a given forest site, this result shows that a simple height-diameter allometry is needed to accurately estimate biomass and carbon stock from plot inventories.     

川西亚高山暗针叶林恢复初期土壤酶活性研究

研究以川西亚高山暗针叶林恢复过程中初级阶段的鲜类阔叶林（MBLF）、鲜类针阔混交林（MCBLF）和箭竹阔叶林（BBLF）土壤为研究对象,针对不同森林类型采用多点分层[0-10 cm（A层）、10-20 cm（B层）、20-30 cm（C层）和30-40 cm（D层）]采样、测定混合样的方式,研究了不同森林类型不同土层土壤的pH值、有机碳、全氮、全磷含量及脲酶、酸性磷酸酶、淀粉酶、过氧化氢酶、蛋白酶和蔗糖酶活性。研究结果表明,MBLF、MCBLF、BBLF不同土层土壤pH值均低于5.0,有机碳、全氮、全磷含量呈下降趋势,且有机碳、全氮与A层土壤差异显著（P〈0.05）。MBLF和BBLF随土层深度的增加脲酶活性较A层呈显著下降趋势（P〈0.05）,而MCBLF土壤脲酶活性以B、C层活性最高,分别为A层的1.22和1.08倍。蔗糖酶和酸性磷酸酶活性呈现先升后降的趋势,均以MCBLF活性最高;在林型MCBLF和BBLF中,随着土层深度的增加,蛋白酶和淀粉酶活性较A层显著降低（P〈0.05）,而MBLF、MCBLF、BBLF过氧化氢酶活性则一直呈显著下降趋势（P〈0.05）,D层过氧化氢酶活性分别为A层的60.21%、73.37%和46.84%。     

Temporal and spatial distribution of three supralittoral amphipod species on a sandy beach of central Italy

The distribution of three talitrid species—Talitrus saltator (Montagu, 1808), Orchestia gammarella (Pallas, 1766), Platorchestia platensis (Kroyer, 1845)—in the beach-dune system at the mouth of the Mignone River (central Italy) was analysed. It was related to the variations of the following abiotic factors: temperature, penetrability, pH, conductivity and moisture of the sediment. The beach-dune system is influenced by human impact and natural erosion. All species showed a maximum capture frequency in November, while the abundance decreased to a minimum in July. T. saltator was dominant on the beach, the other two along the riverbank. Juveniles were abundant along the riverbank; they were most abundant in November and almost disappeared in summer. T. saltator was more abundant near the waterline during the hottest months and occupied the inner beach in winter, with occasional presences on the dune. Regression analysis between the abiotic factors and species abundance showed a positive relationship with pH and temperature for T. saltator, while O. gammarella was negatively related to pH. Canonical correspondence analysis (CCA) showed that penetrability, moisture and temperature had the greatest influence on the species. T. saltator was almost entirely confined to the beach transects and strongly associated with penetrability, temperature and pH values. O. gammarella was mostly associated with the riverbank and P. platensis only found there and on a nearby pool. The two species were mainly influenced by moisture and variations in grain sizes of the sediment.     

Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model

Species distribution models are usually evaluated with cross-validation. In this procedure evaluation statistics are computed from model predictions for sites of presence and absence that were not used to train (fit) the model. Using data for 226 species, from six regions, and two species distribution modeling algorithms (Bioclim and MaxEnt), I show that this procedure is highly sensitive to "spatial sorting bias": the difference between the geographic distance from testing-presence to training-presence sites and the geographic distance from testing-absence (or testing-background) to training-presence sites. I propose the use of pairwise distance sampling to remove this bias, and the use of a null model that only considers the geographic distance to training sites to calibrate cross-validation results for remaining bias. Model evaluation results (AUC) were strongly inflated: the null model performed better than MaxEnt for 45% and better than Bioclim for 67% of the species. Spatial sorting bias and area under the receiver-operator curve (AUC) values increased when using partitioned presence data and random-absence data instead of independently obtained presence-absence testing data from systematic surveys. Pairwise distance sampling removed spatial sorting bias, yielding null models with an AUC close to 0.5, such that AUC was the same as null model calibrated AUC (cAUC). This adjustment strongly decreased AUC values and changed the ranking among species. Cross-validation results for different species are only comparable after removal of spatial sorting bias and/or calibration with an appropriate null model.     

Modeling and spatial prediction of pre-settlement patterns of forest distribution using witness tree data

At the time of European settlement, land surveys were conducted progressively westward throughout the United States. Outside of the original 13 colonies, surveys generally followed the Public Land Survey system in which trees, called witness trees, were regularly recorded at 1 mi by 1 mi grid intersections. This unintentional sampling provides insight into the composition and structure of pre-European settlement forests, which is used as baseline data to assess forest change following settlement. In this paper, a model for the Public Land Surveys of east central Alabama is developed. Assuming that the locations of trees of each species are realized from independent Poisson processes whose respective log intensities are linear functions of environmental covariates (i.e., elevation, landform, and physiographic province), the species observed at the survey grid intersections are independently sampled from a generalized logistic regression model. If all 68 species found in the survey were included, the model would be highly over-parameterized, so only the distribution of the most common taxon, pines, will be considered at this time. To assess the impact of environmental factors not included in the model, a hidden Gaussian random field shall be added as a random effect. A Markov Chain Monte Carlo algorithm is developed for Bayesian inference on model parameters, and for Bayes posterior prediction of the spatial distribution of pines in east central Alabama. Received: June 2004 / Revised: November 2004     

Modelling the distribution and effect of heavy metals in an aquatic ecosystem

Models are reviewed describing the distribution and effect of heavy metals in an aquatic ecosystem. Since a model used for an impact statement should give the maximum concentration level rather than the seasonal variation, a model focussing on this situation is suggested. The basic differential equations describe (1) the variation in concentration of the toxicant per biomass dry matter in a given trophic level, and (2) the exchange of toxicant between sediment and water. Furthermore, since a substantial part of the heavy metal in an aquatic ecosystem is bound to suspended matter, an equation describing the equilibrium between dissolved and suspended matter must be included.A literature review has been carried out on the parameters used in the above mentioned equations and a demonstration, showing how it is possible to find approximate values for such parameters as excretion coefficient and uptake coefficient on the basis of a relationship between these two parameters and the size of an organism, is given.     

Simulation of water quality in a flood detention area using models of different spatial discretization

Detention areas provide a means to lower peak discharges in rivers by temporarily storing excess water. In the case of extreme flood events, the storage effect reduces the risk of dike failures or extensive inundations for downstream reaches and near the site of abstraction. Due to the large amount of organic matter contained in the river water and the inundation of terrestrial vegetation in the detention area, a deterioration of water quality may occur. In particular, decay processes can cause a severe depletion of dissolved oxygen (DO) in the temporary water body. In this paper, we studied the potential of a water quality model to simulate the DO dynamics in a large but shallow detention area to be built at the Elbe River (Germany). Our focus was on examining the impact of spatial discretization on the model’s performance and usability. Therefore, we used a zero-dimensional (0D) and a two-dimensional (2D) modeling approach in parallel. The two approaches solely differ in their spatial discretization, while conversion processes, parameters, and boundary conditions were kept identical. The dynamics of DO simulated by the two models are similar in the initial flooding period but diverge when the system starts to drain. The deviation can be attributed to the different spatial discretization of the two models, leading to different estimates of flow velocities and water depths. Only the 2D model can account for the impact of spatial variability on the evolution of state variables. However, its application requires high efforts for pre- and post-processing and significantly longer computation times. The 2D model is, therefore, not suitable for investigating various flood scenarios or for analyzing the impact of parameter uncertainty. For practical applications, we recommend to firstly set up a fast-running model of reduced spatial discretization, e.g. a 0D model. Using this tool, the reliability of the simulation results should be checked by analyzing the parameter uncertainty of the water quality model. A particular focus may be on those parameters that are spatially variable and, therefore, believed to be better represented in a 2D approach. The benefit from the application of the more costly 2D model should be assessed, based on the analyses carried out with the 0D model. A 2D model appears to be preferable only if the simulated detention area has a complex topography, flow velocities are highly variable in space, and the parameters of the water quality model are well known.     

Predicting the spatial distribution of mangroves in a South African estuary in response to sea level rise,substrate elevation change and a sea storm event

The spatial distribution of mangroves in the Mngazana Estuary under sea level rise induced by climate change, together with different substrate elevation change scenarios was predicted for 2020, 2050 and 2100. The present inundation frequency tolerance range was from 0.8 to 31.2 %, equivalent to substrate elevation thresholds of 1.1 and 1.7 m amsl. These thresholds were measured by field surveys and analysis of a gauge station situated near the mouth of the estuary. The predictions were based on the assumption that the inundation frequency tolerance range of mangrove stands remains constant in the future. Through the use of a digital elevation model an initial increase of 2.10 ha year^?1 was found in mangrove area between present and 2020 (from 122.6 to 143.6 ha). This was due to habitat becoming available that is currently too compacted for seedling establishment to occur. This compaction resulted from human and cattle traffic for grazing. Thereafter there would be a mean loss of 0.66 ha year^?1 from 2020 through 2100. Landward migration of mangroves would not take place due to the elevation limit of adjacent non-mangrove areas. In addition, the loss rate would increase to 1.01 ha year^?1 under insufficient sediment accretion, but would decrease to 0.18 ha year^?1 under thriving mangroves condition. The analysis of sea storm event in September 2008 showed that local water level increased by 28 cm and maximum affected area was 87.0 ha (about 71 % of mangrove stands). The inundation continued over 5 days. The results indicated that the combination impact of sea level rise, substrate elevation change and sea storm would possibly be a threat to tropical African estuaries with large flat intertidal areas and mangroves.     

Modelling the horizontal spatial structure of planktonic community in Lake Trasimeno (Umbria,Italy) using multivariate geostatistical methods

Spatial heterogeneity of ecological systems has been recognised in recent years as an important ecological feature of an ecosystem, rather than a mere statistical nuisance. However, although considerable interest has been paid to the development of statistical methods for the analysis of spatial environmental data, when in presence of more species or environmental variables common analyses still fail to recognise the necessity of a joint modelling of the whole correlation structure. In this paper, we propose to study the multivariate spatial autocorrelation of a plankton community by making explicit reference to a spatial linear factor model entailing a set of constraints for the spatial structure of the planktonic species. The data set examined come from an intensive 2-day sampling survey performed in July 1991 on Lake Trasimeno (Italy) to investigate the horizontal spatial heterogeneity and distribution of the planktonic community, from small (50 m) to large (1000–10,000 m) scale. The analysis revealed that zooplankton and phytoplankton essentially have different degrees of heterogeneity and different spatial structures which required separate modelling. On the other hand, the similarity of the spatial autocorrelation found within zooplankton and phytoplankton communities, indicates that at the investigated scales of observation the horizontal organisation of both components is not appreciably affected by species-specific behaviours. The analysis of the multivariate spatial patterns emerging from the mapping of the extracted factors suggested an interpretation of the distribution of macrozooplankton and phytoplankton assemblages in terms of planktonic responses to environmental factors of a lake-size scale.     

Modelling dependence structures of extreme wind speed using bivariate distribution: a Bayesian approach

When investigating extremes of weather variables, it is seldom that a single weather station determines the damage, and extremes may be caused from the combined behaviour of several weather stations. To investigate joint dependence of extreme wind speed, a bivariate generalised extreme value distribution (BGEVD) was considered from frequentist and Bayesian approaches to analyse the extremes of component-wise monthly maximum wind speed at selected weather stations in South Africa. In the frequentist approach, the parameters of extreme value distributions (EVDs) were estimated with maximum likelihood, whereas in the Bayesian approach the Markov Chain Monte Carlo (MCMC) technique was used with the Metropolis–Hastings algorithm. The results showed that when fitted to component-wise maxima of extreme weather variables, the BGEVD provided apparent benefits over the univariate method, which allowed information to be pooled across stations and resulted in improved precision of the estimates for the parameters and return levels of the distributions. The paper also discusses a method to construct informative priors empirically using historical data of the underlying process from weather characteristics of four pairs of surrounding weather stations at various distances. The results from the Bayesian analysis showed that posterior inference might be affected by the choice of priors that were used to formulate the informative priors. From the results, it could be inferred that the Bayesian approach provides a satisfactory estimation strategy in terms of precision, compared with the frequentist approach, because it accounts for uncertainty in parameters and return level estimations.