Region of influence method improves macroinvertebrate predictive models in Maryland

Stream biological assessment reflects not just conventional water quality, but an environmental quality that represents the integrity of the stream ecosystem. In Britain, Australia and the United States, macroinvertebrate predictive models were built and applied to stream assessment by employing multivariate analysis. There were variations in these models, where adaptations were made for different regions, but the philosophy underlying the models was similar: employ site classification to predict expected assemblage. Taxon assemblage is predicted from reference groups with similar stream features; these resulting models are RIVPACS-style models. Because every site has to belong to one group in the classification process, each reference group might include some dissimilar sites, and their dissimilarity in taxon assemblage impaired the results of taxon predictions from these models. To avoid this limitation, this study employed a Region-of-Influence-style (ROI-style) modeling method, selecting only similar reference sites and allowing each site to build its own reference group.Three different Region-of-Influence selection schemes were applied to improve the macroinvertebrate predictive model in Maryland: the Assessment by Nearest Neighbour Analysis (ANNA), the Burn's Region of Influence (BROI), and the New Datum Region of Influence (NROI) predictive schemes. The prediction results from ANNA, BROI, and NROI were compared, and the reference selections of each predictive scheme were examined. The comparison showed no preference for the total number of reference sites used by either predictive scheme. The number of reference sites did not correlate to the quality of reference sites used and thus does not control the predictability. The ROI-style model in Maryland had better prediction performance than the RIVPACS-style models, and could improve the bioassessment of streams.     

Use of Community‐Composition Data to Predict the Fecundity and Abundance of Species

Abstract: Species distribution models are critical tools for the prediction of invasive species spread and conservation of biodiversity. The majority of species distribution models have been built with environmental data. Community ecology theory suggests that species co‐occurrence data could also be used to predict current and potential distributions of species. Species assemblages are the products of biotic and environmental constraints on the distribution of individual species and as a result may contain valuable information for niche modeling. We compared the predictive ability of distribution models of annual grassland plants derived from either environmental or community‐composition data. Composition‐based models were built with the presence or absence of species at a site as predictors of site quality, whereas environment‐based models were built with soil chemistry, moisture content, above‐ground biomass, and solar radiation as predictors. The reproductive output of experimentally seeded individuals of 4 species and the abundance of 100 species were used to evaluate the resulting models. Community‐composition data were the best predictors of both the site‐specific reproductive output of sown individuals and the site‐specific abundance of existing populations. Successful community‐based models were robust to omission of data on the occurrence of rare species, which suggests that even very basic survey data on the occurrence of common species may be adequate for generating such models. Our results highlight the need for increased public availability of ecological survey data to facilitate community‐based modeling at scales relevant to conservation.     

构建有机化合物斑马鱼雌激素干扰效应的二元分类模型

计算毒理学方法已成为辅助内分泌干扰物(EDCs)管理的决策支持工具。因此,发展内分泌干扰效应指标的(定量)结构活性关系((Q) SAR)等预测模型对于实现EDCs环境管理具有重要的意义。在雌激素受体(Q) SAR模型研究方面,目前主要针对人、牛、大鼠和小鼠等物种的雌激素受体干扰效应进行了研究,而对鱼等水生生物雌激素受体干扰效应等指标的(Q)SAR模型研究还较少。本研究采用基于欧几里德距离的K最近邻(k NN)分类算法,构建了斑马鱼雌激素受体干扰效应的二元分类模型。结果表明,2个最优模型训练集和验证集的预测准确度(Q)、敏感性(Sn)和特异性(Sp)参数均大于0.93,说明模型具有较好的预测能力。因此,能够用所建模型填补模型应用域内其他化合物缺失的斑马鱼雌激素受体干扰效应定性数据。     

Characterization of biological responses under different environmental conditions: A hierarchical modeling approach

A natural river system is organized as a nested hierarchy of interconnected habitats with specific environmental conditions to which the biological community has adapted. Due to this hierarchical structure, identifying the role of different stressors on the biological community is a formidable task. Efforts trying to link stressors to biological integrity have always been bound to the geographic scale of the selected study area, leading to scale-specific results. In this research, an attempt is made to lift this limitation and develop a hierarchical, scale-sensitive methodology that can identify the significant environmental stressors to the biological community at different scales. Sites with similar background environmental conditions are clustered using self-organizing maps (SOM). This is used to identify stressors which affect the biological community throughout the area of study - called environmental gradients or large-scale stressors. Subsequently, these clusters of similar observations (sampling sites) are progressively sub-divided using environmental variables with a significant but localized effect on the biological community - called small-scale stressors. A parent group of sites is split only when the resulting sub-groups have significantly different biological responses. At the end of this recursive sites decomposition procedure, the original set of observations is organized as a tree of environmentally homogeneous groups of observations characterized by unique biological responses to multiple stressors with different geographic extents. The developed hierarchical analysis methodology has been validated using a large-size dataset of environmental observations from the State of Ohio. Our results show that habitat degradation and increased nutrient loading are the large-scale stressors with a widespread impact in Ohio. Other stressors, such as heavy metals, pH or nitrate concentrations have significant albeit localized effects on biological integrity.     

Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters

Two artificial neural networks (ANNs), unsupervised and supervised learning algorithms, were applied to suggest practical approaches for the analysis of ecological data. Four major aquatic insect orders (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e. EPTC), and four environmental variables (elevation, stream order, distance from the source, and water temperature) were used to implement the models. The data were collected and measured at 155 sampling sites on streams of the Adour–Garonne drainage basin (South-western France). The modelling procedure was carried out following two steps. First, a self-organizing map (SOM), an unsupervised ANN, was applied to classify sampling sites using EPTC richness. Second, a backpropagation algorithm (BP), a supervised ANN, was applied to predict EPTC richness using a set of four environmental variables. The trained SOM classified sampling sites according to a gradient of EPTC richness, and the groups obtained corresponded to geographic regions of the drainage basin and characteristics of their environmental variables. The SOM showed its convenience to analyze relationships among sampling sites, biological attributes, and environmental variables. After accounting for the relationships in data sets, the BP used to predict the EPTC richness with a set of four environmental variables showed a high accuracy (r=0.91 and r=0.61 for training and test data sets respectively). The prediction of EPTC richness is thus a valuable tool to assess disturbances in given areas: by knowing what the EPTC richness should be, we can determine the degree to which disturbances have altered it. The results suggested that methodologies successively using two different neural networks are helpful to understand ecological data through ordination first, and then to predict target variables.     

Local and meso-scale patterns of recruitment and abundance of two intertidal crab species that compete for refuges

Interference competition for limited habitat or refuges is known to produce density-dependent mortality and generate patterns of micro-habitat distribution. While in mobile species the outcome of interference at a local scale can usually be determined from differences in body size and behavior, the population-level consequences of such interactions vary depending on rates of settlement and recruitment at a site, which are not directly correlated to local reproductive success. Previous experimental studies in central Chile demonstrated that interference competition for refuges is the primary factor driving microhabitat segregation between the predatory crabs Acanthocyclus gayi and Acanthocyclus hassleri, with the latter species monopolizing galleries inside mussel beds and excluding A. gayi to rock crevices. Between April 2001 and March 2006 we quantified monthly recruitment rates in artificial collectors at 17 sites over 900 km of the central coast of Chile. Results show that recruitment rates of A. hassleri are almost two orders of magnitude lower than those of A. gayi, and that they are tightly and positively correlated among sites across the region, suggesting that at scales of kilometers larval stages of these species are affected by similar oceanographic processes. Total crab densities per site were also positively correlated between species and strongly associated to mussel cover, with overall low crab densities at all sites where mussel cover was lower than about 60%. At all sites with mussel cover >60%, the ratio of A. gayi to A. hassleri density progressively decreased from recruits (2.6) to juveniles (0.5) to adults (0.04), overcoming initial differences in recruitment rates. The relative success of the inferior competitor at sites with low mussel cover does not appear to provide a potential mechanism favoring regional coexistence through dispersal to other sites (“mass effects”), because their densities were lower than at sites of high mussel cover. Yet, at many sites of low mussel cover the dominant competitor is virtually absent, allowing A. gayi to attain larger population sizes at the scale of the region. Thus, the factors limiting the dominant competitor from successfully utilizing other microhabitats seem to be the most critical factor in promoting both local and regional coexistence between these species.     

Bayesian Small Area Models for Assessing Wildlife Conservation Risk in Patchy Populations

Abstract:  Species conservation risk assessments require accurate, probabilistic, and biologically meaningful maps of population distribution. In patchy populations, the reasons for discontinuities are not often well understood. We tested a novel approach to habitat modeling in which methods of small area estimation were used within a hierarchical Bayesian framework. Amphibian occurrence was modeled with logistic regression that included third-order drainages as hierarchical effects to account for patchy populations. Models including the random drainage effects adequately represented species occurrences in patchy populations of 4 amphibian species in the Oregon Coast Range (U.S.A.). Amphibian surveys from other locations within the same drainage were used to calibrate local drainage-scale effects. Cross-validation showed that prediction errors for calibrated models were 77% to 86% lower than comparable regionally constructed models, depending on species. When calibration data were unavailable, small area and regional models performed similarly, although poorly. Small area estimation models complement wildlife ecology and habitat studies, and can help managers develop a regional picture of the conservation status for relatively rare species.     

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.     

Nest selection by snow petrels Pagodroma nivea in East Antarctica: Validating predictive habitat selection models at the continental scale

Little is known on the factors controlling distribution and abundance of snow petrels in Antarctica. Studying habitat selection through modeling may provide useful information on the relationships between this species and its environment, especially relevant in a climate change context, where habitat availability may change. Validating the predictive capability of habitat selection models with independent data is a vital step in assessing the performance of such models and their potential for predicting species’ distribution in poorly documented areas.From the results of ground surveys conducted in the Casey region (2002–2003, Wilkes Land, East Antarctica), habitat selection models based on a dataset of 4000 nests were created to predict the nesting distribution of snow petrels as a function of topography and substrate. In this study, the Casey models were tested at Mawson, 3800 km away from Casey. The location and characteristics of approximately 7700 snow petrel nests were collected during ground surveys (Summer 2004–2005). Using GIS, predictive maps of nest distribution were produced for the Mawson region with the models derived from the Casey datasets and predictions were compared to the observed data. Models performance was assessed using classification matrixes and Receiver operating characteristic (ROC) curves. Overall correct classification rates for the Casey models varied from 57% to 90%. However, two geomorphologically different sub-regions (coastal islands and inland mountains) were clearly distinguished in terms of habitat selection by Casey model predictions but also by the specific variations in coefficients of terms in new models, derived from the Mawson data sets. Observed variations in the snow petrel aggregations were found to be related to local habitat availability.We discuss the applicability of various types of models (GLM, CT) and investigate the effect of scale on the prediction of snow petrel habitats. While the Casey models created with data collected at the nest scale did not perform well at Mawson due to regional variations in nest micro-characteristics, the predictive performance of models created with data compiled at a coarser scale (habitat units) was satisfactory. Substrate type was the most robust predictor of nest presence between Casey and Mawson. This study demonstrate that it is possible to predict at the large scale the presence of snow petrel nests based on simple predictors such as topography and substrate, which can be obtained from aerial photography. Such methodologies have valuable applications in the management and conservation of this top predator and associated resources and may be applied to other Antarctic, Sub-Antarctic and lower latitudes species and in a variety of habitats.     

Use of Coarse‐Resolution Models of Species’ Distributions to Guide Local Conservation Inferences

Abstract: Distribution models are used increasingly for species conservation assessments over extensive areas, but the spatial resolution of the modeled data and, consequently, of the predictions generated directly from these models are usually too coarse for local conservation applications. Comprehensive distribution data at finer spatial resolution, however, require a level of sampling that is impractical for most species and regions. Models can be downscaled to predict distribution at finer resolutions, but this increases uncertainty because the predictive ability of models is not necessarily consistent beyond their original scale. We analyzed the performance of downscaled, previously published models of environmental favorability (a generalized linear modeling technique) for a restricted endemic insectivore, the Iberian desman (Galemys pyrenaicus), and a more widespread carnivore, the Eurasian otter (Lutra lutra), in the Iberian Peninsula. The models, built from presence–absence data at 10 × 10 km resolution, were extrapolated to a resolution 100 times finer (1 × 1 km). We compared downscaled predictions of environmental quality for the two species with published data on local observations and on important conservation sites proposed by experts. Predictions were significantly related to observed presence or absence of species and to expert selection of sampling sites and important conservation sites. Our results suggest the potential usefulness of downscaled projections of environmental quality as a proxy for expensive and time‐consuming field studies when the field studies are not feasible. This method may be valid for other similar species if coarse‐resolution distribution data are available to define high‐quality areas at a scale that is practical for the application of concrete conservation measures.     

Elemental concentrations in air, water, and aquatic biota in two rural provinces in northern Vietnam

The present study on environmental pollution in northern Vietnam investigates elemental concentrations in fine particulate matter (PM_2.5), freshwater, and aquatic biota at two sites with differing levels of industrial activities. An Thin is situated 47 km east of Hanoi in the neighbourhood of a coal combustion power plant, whereas the reference site, Duy Minh, is situated in the agricultural province of Ha Nam, 40 km south of Hanoi. Elemental concentrations were analysed using energy-dispersive X-ray fluorescence, total reflection X-ray fluorescence, and graphite furnace atomic absorption spectro-metry. All investigated elements in fine particles (PM_2.5) had significantly higher concentrations in An Thin, thus identifying the air at this site as polluted. In contrast to the aerosol samples, elemental concentrations as well as quantitative differences between the sampling sites were low in freshwater and biota, indicating that the impact of atmospheric deposition was limited.     

Hierarchical analysis of species distributions and abundance across environmental gradients

Abiotic and biotic processes operate at multiple spatial and temporal scales to shape many ecological processes, including species distributions and demography. Current debate about the relative roles of niche-based and stochastic processes in shaping species distributions and community composition reflects, in part, the challenge of understanding how these processes interact across scales. Traditional statistical models that ignore autocorrelation and spatial hierarchies can result in misidentification of important ecological covariates. Here, we demonstrate the utility of a hierarchical modeling framework for testing hypotheses about the importance of abiotic factors at different spatial scales and local spatial autocorrelation for shaping species distributions and abundances. For the two orchid species studied, understory light availability and soil moisture helped to explain patterns of presence and abundance at a microsite scale (<4 m2), while soil organic content was important at a population scale (<400 m2). The inclusion of spatial autocorrelation is shown to alter the magnitude and certainty of estimated relationships between abundance and abiotic variables, and we suggest that such analysis be used more often to explore the relationships between species life histories and distributions. The hierarchical modeling framework is shown to have great potential for elucidating ecological relationships involving abiotic and biotic processes simultaneously at multiple scales.     

Identifying taxonomic and functional surrogates for spring biodiversity conservation

Surrogate approaches are widely used to estimate overall taxonomic diversity for conservation planning. Surrogate taxa are frequently selected based on rarity or charisma, whereas selection through statistical modeling has been applied rarely. We used boosted‐regression‐tree models (BRT) fitted to biological data from 165 springs to identify bryophyte and invertebrate surrogates for taxonomic and functional diversity of boreal springs. We focused on these 2 groups because they are well known and abundant in most boreal springs. The best indicators of taxonomic versus functional diversity differed. The bryophyte Bryum weigelii and the chironomid larva Paratrichocladius skirwithensis best indicated taxonomic diversity, whereas the isopod Asellus aquaticus and the chironomid Macropelopia spp. were the best surrogates of functional diversity. In a scoring algorithm for priority‐site selection, taxonomic surrogates performed only slightly better than random selection for all spring‐dwelling taxa, but they were very effective in representing spring specialists, providing a distinct improvement over random solutions. However, the surrogates for taxonomic diversity represented functional diversity poorly and vice versa. When combined with cross‐taxon complementarity analyses, surrogate selection based on statistical modeling provides a promising approach for identifying groundwater‐dependent ecosystems of special conservation value, a key requirement of the EU Water Framework Directive.     

PIXGRO: A model for simulating the ecosystem CO2 exchange and growth of spring barley

A model, PIXGRO, developed by coupling a canopy flux sub-model (PROXEL_NEE; PROcess-based piXEL Net Ecosystem CO₂ Exchange) to a vegetation structure submodel (CGRO), for simulating both net ecosystem CO₂ exchange (NEE) and growth of spring barley is described. PIXGRO is an extension of the stand-level CO₂ and H₂O-flux model PROXEL_NEE, that simulates the NEE on a process basis, but goes further to include the dry matter production, partitioning, and crop development for spring barley. Dry matter partitioned to the leaf was converted to leaf area index (LAI) using relationships for the specific leaf area (SLA). The canopy flux component, PROXEL_NEE was calibrated using information from the literature on C3 plants and was tested using CO₂ flux data from an eddy-covariance (EC) method in Finland with long-term observations. The growth component (CGRO) was calibrated using data from the literature on spring barley as well as data from the Finland site. It was then validated against field data from two sites in Germany and partly via the use of MODIS remotely sensed LAI from the Finland site.Both the diurnal and the seasonal patterns of gross CO₂ uptake were very well simulated (R² = 0.92). A slight seasonal bias may be attributed to leaf ageing. Crop growth was also well simulated; simulated dry matter agreed with field observed data from Germany (R² = 0.90). For LAI, the agreement between the simulated and observed was good (R² = 0.80), giving an indication that functions describing the conversion of fixed CO₂ to dry matter and the subsequent partitioning leaf dry matter and LAI simulation were robust and provided reliable estimates.The MODIS LAI at a resolution of 1000 m agreed poorly (R² = 0.45) with the PIXGRO simulated LAI and the observed LAI at the Finland site in 2001. We attributed this to the coarse resolution of the image and/or the small size of the barley field (about 17 ha or 0.25 km²) at the Finland site. By deriving a regression relation between the observed LAI and NDVI from a higher resolution MODIS (500 m resolution), the MODIS-recalculated LAI agreed better with the PIXGRO-simulated LAI (R² = 0.86).PIXGRO provides a prototype model bridging the disciplines of plant physiology, crop modeling and remote sensing, for use in a spatial context in evaluating carbon balances and plant growth at stand level, landscape, regional, and with some care, continental scales. Since almost 50% of the European land surface is covered by crops, such a model is needed for the dynamic estimation of LAI and NEE of croplands.     

Integrating intraseasonal grassland dynamics in cross-scale distribution modeling to support waterbird recovery plans

Despite much discussion about the utility of remote sensing for effective conservation, the inclusion of these technologies in species recovery plans remains largely anecdotal. We developed a modeling approach for the integration of local, spatially measured ecosystem functional dynamics into a species distribution modeling (SDM) framework in which other ecologically relevant factors are modeled separately at broad scales. To illustrate the approach, we incorporated intraseasonal water-vegetation dynamics into a cross-scale SDM for the Common Snipe (Gallinago gallinago), which is highly dependent on water and vegetation dynamics. The Common Snipe is an Iberian grassland waterbird characteristic of European agricultural meadows and a member of one of the most threatened bird guilds. The intraseasonal dynamics of water content of vegetation were measured using the standard deviation of the normalized difference water index time series computed from bimonthly images of the Sentinel-2 satellite. The recovery plan for the Common Snipe in Galicia (northwestern Iberian Peninsula) provided an opportunity to apply our modeling framework. Model accuracy in predicting the species’ distribution at a regional scale (resulting from integration of downscaled climate projections with regional habitat–topographic suitability models) was very high (area under the curve [AUC] of 0.981 and Boyce's index of 0.971). Local water-vegetation dynamic models, based exclusively on Sentinel-2 imagery, were good predictors (AUC of 0.849 and Boyce's index of 0.976). The predictive power improved (AUC of 0.92 and Boyce's index of 0.98) when local model predictions were restricted to areas identified by the continental and regional models as priorities for conservation. Our models also performed well (AUC of 0.90 and Boyce's index of 0.93) when projected to updated water-vegetation conditions. Our modeling framework enabled incorporation of key ecosystem processes closely related to water and carbon cycles while accounting for other factors ecologically relevant to endangered grassland waterbirds across different scales, allowed identification of priority areas for conservation, and provided an opportunity for cost-effective recovery planning by monitoring management effectiveness from space.     

Can spatial isolation help predict dispersal-limited sites for native species restoration?

When the distribution of species is limited by propagule supply, new populations may be initiated by seed addition, but identifying suitable sites for efficiently targeted seed addition remains a major challenge for restoration. In addition to the biotic or abiotic variables typically used in species distribution models, spatial isolation from conspecifics could help predict the suitability of unoccupied sites. Site suitability might be expected to increase with spatial isolation after other factors are accounted for, since isolation increases the chance that a site is unoccupied only because of propagule limitation. For two native annual forbs in Californian grasslands, we combined experimental seeding and niche modeling to ask whether suitability of unoccupied sites could be predicted by spatial variables (either distances from, or densities of, conspecific populations), either by themselves or in combination with niche models. We also asked whether experimental tests of these predictions held up not only in the short term (one year), but also in the longer term (three years). For Lasthenia californica, seed additions were only successful relatively near existing populations. For Lupinus nanus, seeding success was low and was positively related to the number of conspecifics within 1 km. For both species, a few previously unoccupied sites remained occupied three years after seeding, but this subset was not predictable based on either spatial or niche variables. Seed addition alone may be a limited means of native forb restoration if suitable unoccupied sites are either rare or unpredictable, or if they tend to be close to where the species already occurs.     

Community structure and biotic integrity of aquatic plants along the North Canal in Beijing北大核心CSCD

Aquatic plants along the North Canal in Beijing were studied to identify the community structure of aquatic plants and vegetation index of biotic integrity (VIBI), and to provide scientific basis for the management and protection of urban rivers. Aquatic plants from 49 sampling sites along the North Canal were investigated during June 2015. Based on the field data, distributing range analysis, discriminatory power analysis, and correlation analysis were used stepwise to select core metrics from candidate metrics to establish the VIBI assessment system. The VIBI value of each sampling site was calculated as the average of the scaled values of all core metrics. Thirty-six aquatic plant species, including 14 hygrophytes, 13 emergent species, 6 submergent species, 2 floating-leaved species, and 1 floating species were collected. Species diversity was low in the North Canal, and no aquatic plants were recorded in 28 sampling sites, of which 9 sampling sites were dried up. Five sites were in excellent condition (VIBI > 0.60), 5 were good (0.60 > VIBI > 0.38), 7 were fair (0.38 > VIBI > 0.23), and 4 were poor (VIBI < 0.23). Based on the distribution of VIBI, Shahe River and Wenyu River upstream, and Fenghe River located in suburbs had a higher VIBI. Downstream tributaries, such as Qinghe River, Bahe River, and Liangshui River, had a lower VIBI. Correlation analysis showed that habitat quality, habitat complexity, and vegetation diversity along riparian zones were the important factors affecting VIBI along the North Canal, Beijing. Aquatic plants along the North Canal showed low species diversity owing to human disturbance. VIBI along tributaries with limited disturbance from human activities was higher; however, VIBI along tributaries disturbed by frequent human activities was lower. © 2018 Science Press. All rights reserved.     

Scales of similarity in soft sediment epibenthic assemblages: implications for marine protected area design

Remote videography was used to investigate relationships between biological similarity and distance in Moreton Bay, Australia at site (1 km) and local (10 km) scales, and for separate biotic groups. Numerical analysis using Mantels tests to compare distance and similarity matrices showed that at both scales there was a negative correlation between similarity and distance, in that sites further apart were less similar than sites close together. The relationship, although significant (p <0.001), was quite weak (R²=5%) at the site-scale, with no significant (ANOVA with Tukeys pairwise comparisons, p >0.05) decline in similarity up to distances of 2.1–2.6 km. At the local-scale, between-site similarity was high (mean Bray-Curtis similarity >30% for 4th root transformed data) at scales of 10 km or less, and declined markedly with increasing distance. Scales of similarity for different broad taxonomic and functional groups within Moreton Bay were broadly consistent between groups and with the complete dataset. There was evidence of patchiness in the distributions of seagrass and anthozoans at scales less than 16 km. In other biotic groups there was an essentially monotonic decline in similarity with distance. The study showed that the spatial classification approach to habitat mapping is valid in this case, and that site spacing of less than 10 km is necessary to capture important components of biological similarity. Site spacing of less than 2.5 km does not appear to be warranted to capture additional components of biological similarity at the scales studied.Communicated by G.F. Humphrey, Sydney     

Analyzing Variability and the Rate of Decline of Migratory Shorebirds in Moreton Bay,Australia

Abstract: Estimating the abundance of migratory species is difficult because sources of variability differ substantially among species and populations. Recently developed state‐space models address this variability issue by directly modeling both environmental and measurement error, although their efficacy in detecting declines is relatively untested for empirical data. We applied state‐space modeling, generalized least squares (with autoregression error structure), and standard linear regression to data on abundance of wetland birds (shorebirds and terns) at Moreton Bay in southeast Queensland, Australia. There are internationally significant numbers of 8 species of waterbirds in the bay, and it is a major terminus of the large East Asian‐Australasian Flyway. In our analyses, we considered 22 migrant and 8 resident species. State‐space models identified abundances of 7 species of migrants as significantly declining and abundance of one species as significantly increasing. Declines in migrant abundance over 15 years were 43–79%. Generalized least squares with an autoregressive error structure showed abundance changes in 11 species, and standard linear regression showed abundance changes in 15 species. The higher power of the regression models meant they detected more declines, but they also were associated with a higher rate of false detections. If the declines in Moreton Bay are consistent with trends from other sites across the flyway as a whole, then a large number of species are in significant decline.     

模拟酸雨对工业污染场地表层土壤中多环芳烃释放的影响

通过工业污染场地表层土壤的模拟酸雨浸泡试验,分析了不同酸度的模拟酸雨浸泡前后土壤中有机质、EPA优先控制的16种多环芳烃含量和矿物质组成的变化.研究结果表明,酸雨浸泡前后土壤矿物相组成相似,主要以石英为主,只是在矿物组成的量上存在差别,浸泡后土壤中赤铁矿和粘土矿物的含量较浸泡前有所减少.模拟酸雨浸泡后土壤中有机质和多环芳烃均有不同程度的释放,酸雨pH值越小,释放量越大,且多环芳烃可能是随着有机质一起释放的;酸雨对土壤中不同性质多环芳烃释放的影响不同,对低环多环芳烃（环数≤3）释放的影响较大,对高环多环芳烃（环数≥4）影响较小.研究结果为理解在酸雨作用下工业污染场地土壤中多环芳烃的释放规律及土壤中多环芳烃稳定性研究提供一些科学依据.