Developing an index of wetland condition from ecological data: An example using HGM functional variables from the Nanticoke watershed,USA

Demand for data on the ecological condition of wetlands is increasing as state and federal management programs recognize its value in reporting on the ambient condition of the resource, targeting restoration and protection efforts, evaluating the effects of mitigation and restoration practices, supporting regulatory decisions, and tracking the impact of land use decisions. We developed an approach for generating a single measure of wetland condition from ecological variables used in hydrogeomorphic (HGM) assessment. An Index of Wetland Condition (IWC) was developed from HGM field data collected to assess freshwater, non-tidal flat, riverine, and depression wetlands in the Nanticoke River watershed. The HGM variables were screened and scored based on a range check, responsiveness, and metric redundancy, employing a method used to develop indices of biotic integrity. Weights of the individual variables were adjusted to reflect our understanding of wetland ecology and to include variables that represented the vegetation, hydrology, and buffer of a wetland. The final IWC score discriminated high, medium, and low site disturbance classes in flat and riverine wetlands and high and low disturbance classes in depressions, one-way ANOVA F-values ranged from 44.5 to 79.1 (all p <0.0001). The combination of the IWC and HGM assessments provides a comprehensive evaluation of the wetland resource. HGM produces information on specific wetland functions. The IWC concisely conveys the ecological condition of the resource and maximizes the utility of the data collected in an HGM assessment.     

Aboveground decomposition dynamics in riparian depression and slope wetlands of central Pennsylvania

We examined two types of groundwater-fed wetlands (riparian depressions and slopes) classified using the hydrogeomorphic (HGM) system. These wetland types had previously been shown to differ hydrologically. Our first objective was to determine if HGM was a useful structuring variable when examining aboveground decomposition dynamics (rate of mass loss and rate of nitrogen loss). Our second objective was to determine what soil variables were related to any differences in aboveground decomposition dynamics we might find regardless of HGM subclass. We used the litterbag field bioassay technique, and employed a standard litter type (Phalaris arundinacea) across all wetlands. Our results indicated that HGM would not readily serve as an adequate structuring variable for aboveground decomposition in riparian depressions and slope wetlands of central Pennsylvania. Discriminant analysis and classification and regression tree (CART) modeling found soil cation exchange capacity, soil pH, soil organic matter, and soil % nitrogen to be potentially important soil variables related to mass loss, and soil % nitrogen and soil pH to be potentially important variables related to nitrogen loss rate.     

HGM: a call for model validation

The hydrogeomorphic (HGM) approach to wetland functional assessment has been in place for more than 20 years, yet most models developed to date have not been validated. Although HGM models are typically calibrated to a data set, we lack true validation efforts, especially as wetland scientists and managers continue to assert that measuring structure equates to an assessment of function. This paper is a call for a renewed effort at HGM validation in order to fully assure that we know when structure is measured we are also measuring function.     

Multiscale assessment of the influence of habitat structure and composition on bird assemblages in boreal forest

The relative contributions of habitat structure and composition to biodiversity are often scale-dependent. Although bird communities in boreal forest have been largely altered and threatened by forest harvesting, bird habitat selection in this ecosystem has not been fully understood. Our study aimed to assess the relative contributions of habitat structure and composition on the assemblages of boreal birds at multiple spatial scales characterized by radii ranging from 100 to 1,000?m. We recorded bird species occurrence at 96 stations located in an old-growth forest in the C?te-Nord region of Québec, Canada. We characterized habitat structure using the proportion of dense, open, and sparse stands, and habitat composition using the proportions of coniferous, mixedwood, and deciduous stands. We used partial canonical correspondence analyses and hierarchical variance partitioning to assess the relative contribution of habitat structure and composition on bird assemblage, and logistic regression to model the probability of occurrence for individual species in response to habitat variables. Our results revealed that habitat structure and composition explained similar proportions of the variance in bird assemblage (21.7 vs. 21.6?%), regardless of spatial scale. Whilst logistic regression yielded fair predictions in the occurrence of individual species (i.e., area under the receiver-operating characteristic curve >0.70 for 90?% of the species), it further confirmed our findings in community level analysis. Our study indicates that habitat structure and composition are both important in shaping bird assemblages, but spatial scale draws little influence on their relative contributions.     

城市化背景下小微湿地景观动态变化及其驱动因素

小微湿地是指自然界在长期演变过程中形成的小型湿地。城市发展导致小微湿地大量消失，了解小微湿地景观动态变化特征及驱动因素是保护与管理小微湿地的重要基础。以合肥市包河区为研究区域，选取2006-2018年4期遥感影像，利用空间分析方法确定2006-2010年、2010-2014年、2014-2018年、2006-2018年4个时期小微湿地景观动态变化，基于300m×300m的网格单元，结合增强回归树和地理加权逻辑回归模型，分析13个预测变量与小微湿地损失之间的关系。结果表明：2006-2018年，小微湿地在整个研究区大范围减少，总面积下降了60.8%，斑块数量减少了60.5%，同时，小微湿地边缘复杂度降低，小微湿地间的空间距离增加，聚集程度降低。增强回归树模型显示，城市发展初期(2006-2010年)，周边用地类型(建设用地、旱地、林地和草地)变化是导致小微湿地损失的主要因素，中后期(2010-2018年)各类型土地利用变化的相对影响有不同程度的下降，斑块面积和坡度对小微湿地损失的驱动作用逐渐凸显。2006-2018年，建设用地变化(14.4%)、斑块面积(13.5%)、旱地变化(11.1%)、坡度(10.1%)、林地变化(8.5%)、草地变化(7.0%)是导致小微湿地损失的高重要性变量。地理加权逻辑回归模型揭示了高重要性变量对小微湿地损失影响的空间非平稳性特征，结果显示，除斑块面积系数的空间可视化无解释意义，其余高重要性变量对小微湿地损失的影响随地点的变化，贡献的大小和方向也有所不同。研究方法和结果可以为城市快速发展地区小微湿地的保护与管理提供理论支持。     

Comparing tiers of a multi-tiered wetland assessment in the Prairie Pothole Region

Wetland assessment has been shown to be an important tool in understanding the condition and function of the world’s wetlands, and use of muli-tiered assessment strategy has been recommended. In order to evaluate the performance of each tier of a multi-tiered wetland assessment strategy, we sampled 255 seasonally-ponded wetlands in the Missouri Coteau, the most wetland dense ecoregion in the Prairie Pothole Region. We assessed the condition of each study wetland using four sampling methods and models (tiers) of increasing levels of effort and complexity: (1) a level 1 assessment using the geographic information system-based Landscape Wetland Condition Analysis Model (LWCAM); (2) a level 2 assessment using the North Dakota Rapid Assessment Method (NDRAM); (3) a level 3- assessment using the vegetative-based Floristic Quality Index (FQI) and (4) a level 3 assessment consisting of a Hydrogeomorphic (HGM) Model functional assessment. We compared assessment tiers to determine how similar the different levels of assessment ranked sites either by condition or function. Both the NDRAM and FQI assessments, though very different in wetland characteristics assessed, provided similar condition rankings as the more intensive level 3 HGM assessment (89 and 90% similar, respectively). Additionally, the FQI was 86% similar to the level 2 NDRAM, indicating that these two assessment methods have utility in assessing wetlands similar to a HGM assessment. Information from this study can be used as a tool for determining need specific, financial, and time appropriate wetland sampling methods.     

The hydrogeomorphic approach to functional assessment of riparian wetlands: evaluating impacts and mitigation on river floodplains in the U.S.A.

1. The ’hydrogeomorphic‘ approach to functional assessment of wetlands (HGM) was developed as a synthetic mechanism for compensatory mitigation of wetlands lost or damaged by human activities. The HGM approach is based on: (a) classification of wetlands by geomorphic origin and hydrographic regime (b) assessment models that associate variables as indicators of function, and (c) comparison to reference wetlands that represent the range of conditions that may be expected in a particular region. In this paper, we apply HGM to riparian wetlands of alluvial rivers. 2. In the HGM classification, riverine wetlands are characterized by formative fluvial processes that occur mainly on flood plains. The dominant water sources are overbank flooding from the channel or subsurface hyporheic flows. Examples of riverine wetlands in the U.S.A. are: bottomland hardwood forests that typify the low gradient, fine texture substratum of the south-eastern coastal plain and the alluvial flood plains that typify the high gradient, coarse texture substratum of western montane rivers. 3. Assessment (logic) models for each of fourteen alluvial wetland functions are described. Each model is a composite of two to seven wetland variables that are independently scored in relation to a reference data set developed for alluvial rivers in the western U.S.A. Scores are summarized by a ’functional capacity index‘ (FCI), which is multiplied by the area of the project site to produce a dimensionless ’functional capacity unit‘ (FCU). When HGM is properly used, compensatory mitigation is based on the FCUs lost that must be returned to the riverine landscape under statutory authority. 4. The HGM approach also provides a framework for long-term monitoring of mitigation success or failure and, if failing, a focus on topical remediation. 5. We conclude that HGM is a robust and easy method for protecting riparian wetlands, which are critically important components of alluvial river landscapes.     

Drivers of Wetland Conversion: a Global Meta-Analysis

Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions.     

Mapping ephemeral wetlands: manual digitisation and logistic regression modelling in Nelson Mandela Bay Municipality,South Africa

Until recently, little research has been conducted on the distribution and structure of ephemeral systems in semi-arid areas. This information is critical for appropriate wetland management and conservation. The Nelson Mandela Bay Municipality is a semi-arid area along the south-eastern coastline of the Eastern Cape Province of South Africa. The Municipality encapsulates a wide range of geological and geomorphological features as well as vegetation types within an area of some 1950 km², providing an ideal area for such research. The distribution and abundance of wetlands were defined, and a logistic regression (LR) model was used to establish whether this modelling technique is viable in semi-arid areas with highly variable rainfall patterns. Wetlands were delineated manually using geographical information systems, high-resolution aerial photographs and environmental data. More than 1700 wetland polygons were identified, with 80% of the systems being categorised as depressions, seeps and wetland flats. Unchannelled (8%) and channelled (7%) valley bottom wetlands and floodplain wetlands (5%) were also identified. The wetland database was then used to create a wetland occurrence probability model. There were 19 environmental variables used to develop the LR model, with eight variables used in the final model output. The predictive capacity of the model was good, with an area under curve value of 0.68 and an overall accuracy of 66%. This indicates that probabilistic wetland models are useful in highly variable environments with high numbers of small (<1 ha) wetlands. Such predictive models provide a tool to assist in improving the accuracy of land cover datasets in semi-arid areas, and can be used to inform management decisions on flood risk areas and key conservation zones. In addition, abiotic variables that are significant in the model output provide an indication of the factors influencing wetland functioning in the region.     

Assessing,with limited resources,the ecological outcomes of wetland restoration: a South African case

Resources for evaluating the ecological outcomes of ecosystem restoration projects are often limited, especially within government‐funded programs. In order to rapidly assess the ecological outcomes of wetland restoration, an improved approach has been developed, which was applied in the assessment of the ecological outcomes at nine restoration sites of South Africa's Working for Wetlands program. The sites encompass a diversity of restoration problems and land use contexts. The approach begins by distinguishing hydrogeomorphic (HGM) units, for which ecological condition is assessed and reported for hydrology, geomorphology, and vegetation pre‐ and post‐restoration. These three components are closely linked but, as demonstrated at some of the sites, may respond differentially to restoration interventions. For most HGM units, overall ecological condition was improved by between 10 and 30%, with the greatest contribution of restoration generally being to the hydrology component. Having determined the integrity and costs of the interventions, cost‐effectiveness is then reported in South African Rands per hectare equivalent restored, which was found to vary by more than an order of magnitude across the HGM units assessed. Cost‐effectiveness must be interpreted in the light of the long‐term integrity of the interventions, the site's landscape context, and the contribution of restoration to ecosystem services provision. Some sites may be considerably less cost‐effective than others, but the cost may nonetheless be justified if the sites make key contributions to ecosystem services provision. The study was conducted in the context of a formative evaluation and the findings are envisaged to improve wetland restoration practice.     

Spatio-temporal co-occurrence of cougars (Felis concolor), wolves (Canis lupus) and their prey during winter: a comparison of two analytical methods

Aims To examine the spatio‐temporal co‐occurrence of cougars (Felis concolor), wolves (Canis lupus), and their prey during winter using monthly (November–March) species–environment relationship models. In addition, to contrast predictions across two methods: logistic regression and Geographic Information System (GIS) image correlation. Location The eastern front ranges of the Canadian Rocky Mountains (south‐central Alberta), approximately 100 km west of Calgary, including portions of Banff National Park and Kananaskis Country. Methods Snow‐tracking data were collected simultaneously for cougars, wolves, elk (Cervus elaphus), and deer (Odocoileus virginianus and O. hemionus) between November and March, 1997–2000. Track data were synthesized in a GIS. Logistic regression and Akaike's information criterion (AIC) were used to select optimal environmental relationship models for each species. We first examined co‐occurrence by iteratively using each species as a dependent variable (presence/absence) in a logistic regression analysis and using all other species track‐density estimates as independent variables. We built predictive surfaces in a GIS using the exponent form of the logistic regression models, and assessed model accuracy with a receiver operating characteristic curve. We then re‐examined co‐occurrence using pairwise correlations of species probability surfaces by month. The correlation results were compared with logistic regression results to illuminate mechanisms of co‐occurrence and to investigate predictive consistency across the two methods. Results Cougars showed a trend in distribution from higher elevation and less rugged terrain in December, to lower elevation and more rugged terrain in March. This trend differed from that for wolves, which showed a more stable affinity for low elevation and less rugged valley bottoms across all months. The logistic regression models indicated variable positive and negative associations of cougars with wolves by month, and changes in prey associations over time. Notably, there was a shift in co‐occurrence for both predators from elk to deer in March. We found high predictive accuracy for all probability surfaces, except for the month of January. Our image comparison showed that spatial co‐occurrence amongst all species increased over winter, except that wolves and cougars were negatively correlated in February. Combining the results of each approach we found that cougars and wolves converged spatially over winter at the landscape scale (i.e. the valley), while showing more discrete use of that space over time and by habitat attributes (e.g. forest cover, topographic complexity, and prey track density). Main conclusions In the Rocky Mountains, the spatial distributions of cougars and wolves converged into the valley floor as winter progressed. Cougars were distinct from wolves and prey in the intensity of this shift. We determined that a comparison of predictive surfaces alone fails to explain species co‐occurrence. The surfaces must be coupled with investigation of respective species–environment models to account for temporal changes in associations. We suggest that the two approaches represent different ecological scales: image comparison may be best for landscape‐ (valley) level analysis, while logistic regression is best for site‐level analysis. Ultimately, both approaches were critical to our analysis. Finally, the variability observed over time suggested that annual and seasonal models may obscure important ecological patterns and processes, especially for cougars.     

Urbanization and a threatened freshwater mussel: evidence from landscape scale studies

The inflated heelsplitter, Potamilus inflatus, a federally listed freshwater bivalve, has been eradicated from northern portions of its range in the Amite River in Louisiana, USA. We hypothesized that the remaining populations of Potamilus inflatus in the southern part of the Amite River are being affected by increased urbanization of the watershed caused by growth of the surrounding Baton Rouge metropolitan area. Comparison of catch per unit effort in 2007 with a study conducted in 1994 indicated a significant drop in CPUE from 1.76 heelsplitters per site to 0.87. The size distribution of heelsplitters also had decreased in mean shell length from 116 to 97 mm, owing either to dislodgement of larger individuals in spates, or die-offs of larger males. Logistic regression suggested that site variables like substrate type and current velocity were not as important as landscape scale variables in predicting heelsplitter presence at a site. Heelsplitter presence was positively related to the amount of wetland riparian forest, and negatively related to the amount of residential development at the reach (1 km upstream) scale. Our results are significant because we show (1) that statistical models of GIS-based land use can predict the distribution of threatened mussel species, and that (2) conservation of endangered freshwater mussels will require more emphasis on the integrity of the riparian corridor.     

基于抚育间伐效应的长白落叶松人工林两阶段枯死模型

1972和1974年分别在黑龙江省江山娇林场及孟家岗林场设置10块长白落叶松人工林固定样地(8块抚育间伐样地、2块对照样地),采用连年复测数据,分析抚育间伐对人工长白落叶松样地枯死与单木枯死的影响.基于二分类变量Logistic回归,建立了样地枯死及样地内单木枯死概率的两阶段模型(Ⅰ:抚育间伐后样地水平枯死概率模型;Ⅱ:枯死样地中单木水平枯死概率模型),采用广义估计方程(GEE)方法对模型参数进行估计.根据敏感度和特异度曲线相交点确定枯死概率最优临界点.结果表明: 样地数据按照抚育间伐次数分为4组分别建模(模型1～模型4).在模型1中,地位指数、林分年龄的自然对数、抚育间伐年龄及强度为显著自变量;模型2～模型4采用主成分分析法建模,主成分包含林分年龄、每公顷株数、平均胸径及抚育间伐因子,说明抚育间伐因子对样地枯死概率有显著影响.抚育间伐对枯死样地中单木枯死概率无显著影响,单木枯死概率模型中显著性自变量为林分初植密度、年龄、林木胸径的倒数及林分中大于对象木的所有林木断面积之和.样地枯死概率模型及单木枯死概率模型Hosmer和Lemeshow拟合优度检验均不显著,模型AUC均在0.91以上,估计正确率均超过80%,说明模型拟合效果较好.     

A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data

In this paper, we propose to use probabilistic neural networks (PNNs) for classification of bacterial growth/no-growth data and modeling the probability of growth. The PNN approach combines both Bayes theorem of conditional probability and Parzen's method for estimating the probability density functions of the random variables. Unlike other neural network training paradigms, PNNs are characterized by high training speed and their ability to produce confidence levels for their classification decision. As a practical application of the proposed approach, PNNs were investigated for their ability in classification of growth/no-growth state of a pathogenic Escherichia coli R31 in response to temperature and water activity. A comparison with the most frequently used traditional statistical method based on logistic regression and multilayer feedforward artificial neural network (MFANN) trained by error backpropagation was also carried out. The PNN-based models were found to outperform linear and nonlinear logistic regression and MFANN in both the classification accuracy and ease by which PNN-based models are developed.     

Probabilistic modeling and mapping of plant indicator species in a Northeast Oregon industrial forest,USA

Managing forest ecosystems for sustainable, multiple use requires forest resource managers to understand and predict how plant species composition and distribution varies across environmental gradients and responds to landscape scale disturbances. This study demonstrates predictive vegetation modeling and mapping for a Northeast Oregon forest using non-parametric Multiplicative Regression (NPMR) with presence/absence data for the species Clintonia uniflora (CLUN) and a set of stand structural and raster-based predictor variables. NPMR is a flexible probability modeling system that can find the best subset of habitat factors influencing species occurrence. NPMR was compared with logistic regression (LR) by building reduced models from variables selected as best by NPMR and full models from variables identified as significant with a forward stepwise process and further manual testing. log β was used to select models with the highest predictive capability. NPMR models were less complex and had higher predictive capability than LR for all modeling approaches. Spatial coordinates were among the most powerful predictors and the modeling approach with physiographic and stand structural variables together was the most improved relative to the average frequency of occurrence. GIS probability maps produced with the application of the physiographic models showed good spatial congruence between high probability values and plots that contained CLUN. NPMR proved to be a reliable probability modeling and mapping tool that could be used as the analytical link between monitoring and quantifying the status and trends of vegetation resources.     

Determination of biologically significant hydrologic condition metrics in urbanizing watersheds: an empirical analysis over a range of environmental settings

We investigated the relations among 83 hydrologic condition metrics (HCMs) and changes in algal, invertebrate, and fish communities in five metropolitan areas across the continental United States. We used a statistical approach that employed Spearman correlation and regression tree analysis to identify five HCMs that are strongly associated with observed biological variation along a gradient of urbanization. The HCMs related to average flow magnitude, high-flow magnitude, high-flow event frequency, high-flow duration, and rate of change of stream cross-sectional area were most consistently associated with changes in aquatic communities. Although our investigation used an urban gradient design with short hydrologic periods of record (≤1 year) of hourly cross-sectional area time series, these five HCMs were consistent with previous investigations using long-term daily-flow records. The ecological sampling day often was included in the hydrologic period. Regression tree models explained up to 73, 92, and 79% of variance for specific algal, invertebrate, and fish community metrics, respectively. National models generally were not as statistically significant as models for individual metropolitan areas. High-flow event frequency, a hydrologic metric found to be transferable across stream type and useful for classifying habitat by previous research, was found to be the most ecologically relevant HCM; transformation by precipitation increased national-scale applicability. We also investigated the relation between measures of stream flashiness and land-cover indicators of urbanization and found that land-cover characteristic and pattern variables, such as road density, percent wetland, and proximity of developed land, were strongly related to HCMs at both a metropolitan and national scale and, therefore, may be effective land-use management options in addition to wholesale impervious-area reduction.     

Can patterns of spatial autocorrelation reveal population processes? An analysis with the fire salamander

Spatial autocorrelation (SAC) is often observed in species distribution data, and can be caused by exogenous, autocorrelated factors determining species distribution, or by endogenous population processes determining clustering such as dispersal. However, it remains debated whether SAC patterns can actually reveal endogenous processes. We reviewed studies measuring dispersal of the salamander Salamandra salamandra, to formulate a priori hypotheses on the scale at which dispersal is expected to determine population distribution. We then tested the hypotheses by analysing SAC in distribution data, and evaluating whether controlling for the effect of environmental variables can reveal endogenous processes. We surveyed 565 streams to obtain species distribution data; we also recorded landscape and microhabitat features known to affect the species. We used multiple approaches to tease apart endogenous and exogenous SAC: the analysis of residuals of logistic regression models considering different environmental variables; the analysis of eigenvectors extracted by several implementations of spatial eigenvector mapping. In capture–mark–recapture studies, 98% of individuals moved 500 m or less. Both species distribution and environmental features were strongly autocorrelated. The residuals of logistic regression relating species to environmental variables were autocorrelated at distances up to 500 m; analyses considering different sets of environmental variables, or assuming non‐linear species habitat relationships, yielded identical results. The results of spatial eigenvector mapping strongly depended on the matrix of distances used. Nevertheless, the eigenvectors of models with best fit were autocorrelated at distances up to 200–500 m. The concordance between multiple approaches suggests that 500 m is the scale at which dispersal connects breeding localities, increasing probability of occurrence. If exogenous variables are correctly identified, the analysis of SAC can provide important insights on endogenous population processes, such as the flow of individuals. SAC analysis can also provide important information for conservation, as the existence of metapopulations or population networks is essential for long term persistence of amphibians.     

Spatially explicit conservation issues for threatened bird species in Mediterranean farmland landscapes

Coupling habitat models based on GIS and on ground variables could help identify suitable areas (by means of landscape models obtained by GIS variables) to concentrate management actions for species’ conservation. In this study, the habitat requirements of Lesser Greys (LGS) and Woodchat Shrikes (WS), two threatened farmland bird species declining in Europe, were assessed in Apulia (south-eastern Italy) by means of binary logistic regression at two different levels: landscape (using GIS-measured variables); and, territory (using ground-measured variables) scales. The LGS occurrence at landscape scale was correlated to steppe-like areas and cereal crops. At the territory level, significant effects were detected for deciduous forests and the presence of isolated trees and shrubs. The WS occurrence at landscape scale was promoted by steppe-like areas and cereal crops, whereas, at the territory level significant effects were detected for steppe-like areas positively and suburban areas negatively. The landscape model was extrapolated to the entire region. Within highly suitable areas (occurrence probability higher than 0.66 according to the landscape model), we measured average habitat features and compared them with the optimal mosaic depicted by the territory level models. This allowed us to give spatially explicit and site-specific management recommendations for these two threatened species. LGS will mostly benefit from an increase in isolated shrubs and trees; whereas for WS, the most widespread recommendations are to increase steppe-like habitat and to prevent further urbanisation.Coupling “coarse” landscape models with the species ecology provided by fine-scaled models can integrate relevant information on species potential distribution and territory level requirements, making planning fine-tuned habitat management (within potentially suitable landscapes) in a spatially explicit way possible.     

Maternal Age-Specific Rates for Trisomy 21 and Common Autosomal Trisomies in Fetuses from a Single Diagnostic Center in Thailand

To provide maternal age-specific rates for trisomy 21 (T21) and common autosomal trisomies (including trisomies 21, 18 and 13) in fetuses. We retrospectively reviewed prenatal cytogenetic results obtained between 1990 and 2009 in Songklanagarind Hospital, a university teaching hospital, in southern Thailand. Maternal age-specific rates of T21 and common autosomal trisomies were established using different regression models, from which only the fittest models were used for the study. A total of 17,819 records were included in the statistical analysis. The fittest models for predicting rates of T21 and common autosomal trisomies were regression models with 2 parameters (Age and Age²). The rate of T21 ranged between 2.67 per 1,000 fetuses at the age of 34 and 71.06 per 1,000 at the age of 48. The rate of common autosomal trisomies ranged between 4.54 per 1,000 and 99.65 per 1,000 at the same ages. This report provides the first maternal age-specific rates for T21 and common autosomal trisomies fetuses in a Southeast Asian population and the largest case number of fetuses have ever been reported in Asians.     

Comparison of Different Risk Perception Measures in Predicting Seasonal Influenza Vaccination among Healthy Chinese Adults in Hong Kong: A Prospective Longitudinal Study

Background

Risk perception is a reported predictor of vaccination uptake, but which measures of risk perception best predict influenza vaccination uptake remain unclear.

Methodology

During the main influenza seasons (between January and March) of 2009 (Wave 1) and 2010 (Wave 2),505 Chinese students and employees from a Hong Kong university completed an online survey. Multivariate logistic regression models were conducted to assess how well different risk perceptions measures in Wave 1 predicted vaccination uptake against seasonal influenza in Wave 2.

Principal Findings

The results of the multivariate logistic regression models showed that feeling at risk (β = 0.25, p = 0.021) was the better predictor compared with probability judgment while probability judgment (β = 0.25, p = 0.029 ) was better than beliefs about risk in predicting subsequent influenza vaccination uptake. Beliefs about risk and feeling at risk seemed to predict the same aspect of subsequent vaccination uptake because their associations with vaccination uptake became insignificant when paired into the logistic regression model. Similarly, to compare the four scales for assessing probability judgment in predicting vaccination uptake, the 7-point verbal scale remained a significant and stronger predictor for vaccination uptake when paired with other three scales; the 6-point verbal scale was a significant and stronger predictor when paired with the percentage scale or the 2-point verbal scale; and the percentage scale was a significant and stronger predictor only when paired with the 2-point verbal scale.

Conclusions/Significance

Beliefs about risk and feeling at risk are not well differentiated by Hong Kong Chinese people. Feeling at risk, an affective-cognitive dimension of risk perception predicts subsequent vaccination uptake better than do probability judgments. Among the four scales for assessing risk probability judgment, the 7-point verbal scale offered the best predictive power for subsequent vaccination uptake.