Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l’Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R² (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R²: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the ‘upland model’ was able to more accurately predict SOC compared with the ‘upland & wetland model’. However, the separately calibrated ‘upland and wetland model’ did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).     

区域尺度的中国植物功能型与生物群区

利用“生态－外貌”原则,中国的现状植被类型及其分布,确定中国的39种优势植物功能型：高山常绿针叶、北方常绿针叶、北方夏绿针叶、冷温带常绿针叶、温带常绿针叶、暖温带常绿阔叶、暖温带硬叶阔叶、暖温带夏绿阔叶、热带常绿阔叶、热带 雨绿阔叶、热带落叶阔叶、暖温带竹、高山／亚高山灌木、温带草原灌木、温带荒漠灌木、冷温带灌木、温带灌木、暖温带灌木、热带灌木、干旱灌木、高山草、荒漠草、温带草原草、温带草、沼泽草、红树、北方农作物、冷温带农作物、温带农作物、暖温带农作物、热带农作物和裸地。再依据优势植物功能型归并中国的21类潜在生物群区：北方（寒温带）落叶林、北方（寒温带）常绿林、冷温带针阔叶混交林、温带落叶阔叶林、暖温带（亚热带）落叶常绿阔叶混交林、暖温带（亚热带）常绿阔叶林、暖温带（亚）常绿阔叶季风林、热带雨林、热带季雨林、热带落叶林、红树林、干旱疏林／稀树草原、;温带草甸／稀树草原、温带草原、温带半草原、温带荒漠、温带半荒漠、高山／高山针叶林、高山／亚高山灌丛／草甸、高山／亚高山草原和高山／亚高山荒漠。如果考虑现状农业植被类型：一年一熟农作物、二年三熟农作物、一年二熟农作物和一年三熟农作物,可归并为25类现状生物群区。这是全球生态学和古生态学研究中区域尺度旧我国植物功能型和生物群区分类的一次尝试。     

Improving the Use of Toxicity Reference Values in Wildlife Food Chain Modeling and Ecological Risk Assessment

Ecological risk assessments often include mechanistic food chain models based on toxicity reference values (TRVs) and a hazard quotient approach. TRVs intended for screening purposes or as part of a larger weight-of-evidence (WOE) assessment are readily available. However, our experience suggests that food chain models using screening-level TRVs often form the primary basis for risk management at smaller industrial sites being redeveloped for residential or urban parkland uses. Iterative improvement of a food chain model or the incorporation of multiple lines of evidence for these sites are often impractical from a cost-benefit perspective when compared to remedial alternatives. We recommend risk assessors examine the assumptions and factors in the TRV derivation process, and where appropriate, modify the TRVs to improve their ecological relevance. Five areas where uncertainty likely contributes to excessively conservative hazard quotients are identified for consideration.     

Effects of Animal Husbandry on Secondary Production and Trophic Efficiency at a Regional Scale

Agricultural systems are expected to have higher net secondary production (NSP) than natural systems as a result of higher trophic efficiency and lower interannual variability. These differences, however, have not been quantified across regional gradients. We compiled a dataset of herbivore biomass, consumption, NSP, annual precipitation, and aboveground net primary production (ANPP) for extensive livestock farms across a wide precipitation gradient in Argentina. We compared these data with worldwide published studies of natural systems. In a double-logarithmic scale, NSP of agricultural systems increased with ANPP from semiarid to subhumid systems and decreased from subhumid to humid systems, a response that contrasted with the linear positive increase of natural systems. Compared to natural systems dominated by homeotherms, E _troph (NSP:ANPP) in agricultural systems in semiarid areas was 8 times higher, due to a 2 times higher E _consump (Consumption:ANPP) and a 4 times higher E _prod (NSP:Consumption). In subhumid areas, E _troph was 46 times higher, due to a 13.7 times higher E _consump and a 3.3 times higher E _prod. In humid areas, E _troph was 5 times higher, due to a 2.5 times higher E _consump and a 2 times higher E _prod. The interannual variation of herbivore biomass, a major determinant of NSP, was 60 % lower in agricultural than in natural systems dominated by homeotherms, and was decoupled from the variability of precipitation. Agricultural systems reach higher NSP by (1) diverting a major proportion of ANPP from the detritus to the grazing chain, (2) converting more efficiently consumption into NSP, and (3) stabilizing herbivore biomass across years.     

Movement-Based Estimation and Visualization of Space Use in 3D for Wildlife Ecology and Conservation

Advances in digital biotelemetry technologies are enabling the collection of bigger and more accurate data on the movements of free-ranging wildlife in space and time. Although many biotelemetry devices record 3D location data with x, y, and z coordinates from tracked animals, the third z coordinate is typically not integrated into studies of animal spatial use. Disregarding the vertical component may seriously limit understanding of animal habitat use and niche separation. We present novel movement-based kernel density estimators and computer visualization tools for generating and exploring 3D home ranges based on location data. We use case studies of three wildlife species – giant panda, dugong, and California condor – to demonstrate the ecological insights and conservation management benefits provided by 3D home range estimation and visualization for terrestrial, aquatic, and avian wildlife research.     

Conservation as It Is: Illicit Resource Use in a Wildlife Reserve in India

While wildlife conservation efforts have become increasingly aggressive around the world, illicit use of resources in conservation areas has not subsided, raising questions about the ecological character of noncompliance activities. This paper reviews the results of research conducted amongst foresters and households living adjacent to a wildlife sanctuary in India in a period following a ban of human use activities. Using a formal survey supplemented by interviews and participant observation, the research sought to determine the intensity of illicit use, the demographics of resource users, and the attitudes of rulebreakers. The results suggest that noncompliance with conservation restrictions is nearly universal, that forest use is highly specialized, that available village resource assets do little to offset forest use, and that rule-breakers prefer current governance arrangements. These results paint a picture of deeply institutionalized forest use that suggests serious barriers to any simple enforcement solutions or governance reforms.     

Sustainable Cost Models for mHealth at Scale: Modeling Program Data from m4RH Tanzania

Background

There is increasing evidence that mobile phone health interventions (“mHealth”) can improve health behaviors and outcomes and are critically important in low-resource, low-access settings. However, the majority of mHealth programs in developing countries fail to reach scale. One reason may be the challenge of developing financially sustainable programs. The goal of this paper is to explore strategies for mHealth program sustainability and develop cost-recovery models for program implementers using 2014 operational program data from Mobile for Reproductive Health (m4RH), a national text-message (SMS) based health communication service in Tanzania.

Methods

We delineated 2014 m4RH program costs and considered three strategies for cost-recovery for the m4RH program: user pay-for-service, SMS cost reduction, and strategic partnerships. These inputs were used to develop four different cost-recovery scenarios. The four scenarios leveraged strategic partnerships to reduce per-SMS program costs and create per-SMS program revenue and varied the structure for user financial contribution. Finally, we conducted break-even and uncertainty analyses to evaluate the costs and revenues of these models at the 2014 user volume (125,320) and at any possible break-even volume.

Results

In three of four scenarios, costs exceeded revenue by $94,596, $34,443, and $84,571 at the 2014 user volume. However, these costs represented large reductions (54%, 83%, and 58%, respectively) from the 2014 program cost of $203,475. Scenario four, in which the lowest per-SMS rate ($0.01 per SMS) was negotiated and users paid for all m4RH SMS sent or received, achieved a $5,660 profit at the 2014 user volume. A Monte Carlo uncertainty analysis demonstrated that break-even points were driven by user volume rather than variations in program costs.

Conclusions

These results reveal that breaking even was only probable when all SMS costs were transferred to users and the lowest per-SMS cost was negotiated with telecom partners. While this strategy was sustainable for the implementer, a central concern is that health information may not reach those who are too poor to pay, limiting the program’s reach and impact. Incorporating strategies presented here may make mHealth programs more appealing to funders and investors but need further consideration to balance sustainability, scale, and impact.     

A Bootstrap-Based Covariate Selection Method for Modeling the Risk of Lightning-Induced Fires at a Local Scale: A Case Study in Northwest Spain

In lightning-induced fire risk prediction models, the number of potential predictors is usually high, with some redundancy among them. It is therefore important to select the best subset of predictors that obtain models with the greatest discrimination capacity. With this aim in mind, the logistic generalized linear model was used to estimate lightning-induced fire occurrence using a case study of the province of León (northwest Spain). A bootstrap-based test was used to obtain the optimal number of predictors and to model this optimal number of predictors displaying the largest area under the receiver operating characteristics curve. The results show that of the 16 variables initially considered, only three were necessary to obtain the model with the best discriminatory capacity for estimating lightning-induced fire occurrence. Moreover, this model can be considered equivalent to another nine alternative models with three covariates. Both the optimal and the equivalent models are useful in the spatially explicit assessment of fire risk, the planning and coordination of regional efforts to identify areas at greatest risk, and the design of long-term wildfire management strategies. The methodology used for this case study can be applied to other wildfire risk assessment situations where multiple and interconnected covariates are available.     

Constructive Contrasts Between Modeled and Measured Climate Responses Over a Regional Scale

Reducing uncertainty in predictions of regional-scale models depends on meaningful contrasts with field measurements. This paper introduces a two-stage process that works from the premise that an appropriate goal for regional models is to produce reasonable behavior over dominant environmental gradients. We demonstrate two techniques for contrasting models with data, one based on the shape of modeled relationships (functional contrasts) and the other based on an examination of the residuals (residual contrasts) between the model and an empirically derived surface fit to field data. Functional contrasts evaluated the differences between the response of simulated net primary production (NPP) to climate variables and the response observed in field measurements of NPP. Residual contrasts compared deviations of NPP from the empirical surface to identify groupings (for example, vegetation classes, geographic regions) with model deviations different from those of the field data. In all model–data contrasts, we assigned sample weights to field measurements to ensure unbiased representation of the region, and we included both constructive comparisons and formal statistical tests. In general, we learned more from constructive methods designed to reveal structure or pattern in discrepancy than we did from statistical tests designed to falsify models. Although our constructive methods were more subjective and less concise, they succeeded in revealing gaps in our understanding of regional-scale processes that can guide future efforts to reduce scientific uncertainty. This was best illustrated by NPP predictions from the Biome-BGC model, which showed a stronger response to precipitation than apparently operates in the field. In another case, differences revealed in savanna and dry woodlands had insufficient field-data support, suggesting a need for future field studies to improve understanding in this, and other, poorly studied ecosystems. Received 24 September 1999; accepted 20 April 2000.     

Use of a Mixture Statistical Model in Studying Malaria Vectors Density

Vector control is a major step in the process of malaria control and elimination. This requires vector counts and appropriate statistical analyses of these counts. However, vector counts are often overdispersed. A non-parametric mixture of Poisson model (NPMP) is proposed to allow for overdispersion and better describe vector distribution. Mosquito collections using the Human Landing Catches as well as collection of environmental and climatic data were carried out from January to December 2009 in 28 villages in Southern Benin. A NPMP regression model with “village” as random effect is used to test statistical correlations between malaria vectors density and environmental and climatic factors. Furthermore, the villages were ranked using the latent classes derived from the NPMP model. Based on this classification of the villages, the impacts of four vector control strategies implemented in the villages were compared. Vector counts were highly variable and overdispersed with important proportion of zeros (75%). The NPMP model had a good aptitude to predict the observed values and showed that: i) proximity to freshwater body, market gardening, and high levels of rain were associated with high vector density; ii) water conveyance, cattle breeding, vegetation index were associated with low vector density. The 28 villages could then be ranked according to the mean vector number as estimated by the random part of the model after adjustment on all covariates. The NPMP model made it possible to describe the distribution of the vector across the study area. The villages were ranked according to the mean vector density after taking into account the most important covariates. This study demonstrates the necessity and possibility of adapting methods of vector counting and sampling to each setting.     

Sources of Variability in Net Primary Production Predictions at a Regional Scale: A Comparison Using PnET-II and TEM 4.0 in Northeastern US Forests

Because model predictions at continental and global scales are necessarily based on broad characterizations of vegetation, soils, and climate, estimates of carbon stocks and fluxes made by global terrestrial biosphere models may not be accurate for every region. At the regional scale, we suggest that attention can be focused more clearly on understanding the relative strengths of predicted net primary productivity (NPP) limitation by energy, water, and nutrients. We evaluate the sources of variability among model predictions of NPP with a regional-scale comparison between estimates made by PnET-II (a forest ecosystem process model previously applied to the northeastern region) and TEM 4.0 (a terrestrial biosphere model typically applied to the globe) for the northeastern US. When the same climate, vegetation, and soil data sets were used to drive both models, regional average NPP predictions made by PnET-II and TEM were remarkably similar, and at the biome level, model predictions agreed fairly well with NPP estimates developed from field measurements. However, TEM 4.0 predictions were more sensitive to regional variations in temperature as a result of feedbacks between temperature and belowground N availability. In PnET-II, the direct link between transpiration and photosynthesis caused substantial water stress in hardwood and pine forest types with increases in solar radiation; predicted water stress was relieved substantially when soil water holding capacity (WHC) was increased. Increasing soil WHC had little effect on TEM 4.0 predictions because soil water storage was already sufficient to meet plant demand with baseline WHC values, and because predicted N availability under baseline conditions in this region was not limited by water. Because NPP predictions were closely keyed to forest cover type, the relative coverage of low- versus high-productivity forests at both fine and coarse resolutions was an important determinant of regional NPP predictions. Therefore, changes in grid cell size and differences in the methods used to aggregate from fine to coarse resolution were important to NPP predictions insofar as they changed the relative proportions of forest cover. We suggest that because the small patches of high-elevation spruce-fir forest in this region are substantially less productive than forests in the remainder of the region, more accurate NPP predictions will result if models applied to this region use land cover input data sets that retain as much fine-resolution forest type variability as possible. The differences among model responses to variations in climate and soil WHC data sets suggest that the models will respond quite differently to scenarios of future climate. A better understanding of the dynamic interactions between water stress, N availability, and forest productivity in this region will enable models to make more accurate predictions of future carbon stocks and fluxes. Received 19 June 1998; accepted 25 June 1999.     

区域尺度的昆虫迁飞行为与害虫间歇性猖獗

60年代以来,随着耕作制度和栽培品种的改变以及高投入丰产措施的普遍推广,我国迁飞性害虫的发生范围不断扩大,猖獗频率逐渐增加,对农业生产的威胁日益严重[1～4]。国内曾多次组织全国性大协作,研究迁飞性害虫的迁飞规律、地方发生规律和测报防治技术,虽然取得了相当的成就,但仍满足不了生产上的要求。目前尚无法以足够的精度预报迁飞性害虫的迁入期、迁入区和迁入量,对灾变的预警能力一直在低水平徘徊。其中一个主要原因就是对迁飞性害虫的消长规律尚缺乏深层次的基础研究,尤其是宏观尺度的机制研究一直相当薄弱,而这恰恰是亟待早日解决的关…     

Fixed Dystonia in Complex Regional Pain Syndrome: a Descriptive and Computational Modeling Approach

Background  

Complex regional pain syndrome (CRPS) may occur after trauma, usually to one limb, and is characterized by pain and disturbed blood flow, temperature regulation and motor control. Approximately 25% of cases develop fixed dystonia. Involvement of dysfunctional GABAergic interneurons has been suggested, however the mechanisms that underpin fixed dystonia are still unknown. We hypothesized that dystonia could be the result of aberrant proprioceptive reflex strengths of position, velocity or force feedback.     

Combined Use of Life Cycle Assessment and Groundwater Transport Modeling to Support Contaminated Site Management

The subject of this study is a spent pot lining (SPL) landfill. The aim of this study was to identify the site remediation option, among four alternatives, that minimizes overall environmental impacts based on: 1) a comparative life cycle assessment (LCA); and 2) modeling of contaminant transport in groundwater. The four options were: leaving the landfill in place (Option 1); excavation of the landfill, with on-site disposal of the excavated materials in a secure cell (Option 2); excavation of the landfill, with treatment of the SPL fraction (Option 3); and excavation of the landfill, with incineration of the SPL fraction in a cement kiln (Option 4). The LCA was performed following the guidelines provided by the International Standard Organization (ISO). Furthermore, to improve the relevance of LCA to site remediation sector, impacts caused by residual in-situ contamination were assessed by applying a simulation of contaminant transport in groundwater, using site-specific data. The LCA identified Option 1 as having the least environmental impacts. However, the transport modeling concluded that contaminant concentrations 50 years from the present could be approximately 30 to 40 times the regulatory criteria if this option is retained. In addition, this study demonstrated that LCA can be used as a screening tool to help identify significant environmental issues; the LCA identified acute and chronic water ecotoxicity categories as being the dominant impact categories of the environmental profile and consequently, it is recommended that a complete environmental risk assessment (ERA) be performed for Option 1.     

West Siberian Latitudinal Xylomycological Scale and Its Use for Indicating Forest Conditions

A quantitative analysis of the aphyllophoroid macromycetes of Betula complex on 207 sites of the territory of the West Siberian Plain in the range from forest tundra to forest steppe allows us to determine the width of the zone and its patterns for the biota of xylotrophic fungi. It is shown that the width and structure of the zonal xylomycocomplexes is determined, first and foremost, by climatic factors, which are characterized by an expressed hydrothermal gradient. They influence fungi directly also by the formation of the forest stand corresponding to climate. The sequence of width and zone xylomycocomplex of the region is a scale for the indication of forest vegetation conditions and parameters of the forest stand of a particular timber stand. Possibilities of indication are expanded with the differentiation of the xylomycocomplex and, respectively, its width and zone scale on functional fractions of fungi (stem, peripheral, and wound types). The scale is used for assessing the condition of woods in the Numto Natural Park (northern taiga). For the characteristic of each site, coefficients of correlation of numerical structure of this mycocenos and the zone xylomycocomplex in the range from the forest tundra to the forest steppe were obtained and calculated for functional groups. The configuration of the growth spectral curves obtained in this way and their mutual position indicates the peculiarities of the hydrothermal regime of a plot; the influence of mechanical damages on tree stands; and their physiological conditions, completeness, quality, and age. It is shown that, according to the results of mycological indication, forest conditions of the Numto National Park forest plots damaged by fires and clearcuttings correspond to forest conditions of the zones to the south of the park. Regularities of the West Siberian latitude and zone xylomycological scale are characterized by a certain universality for the forest biome of Siberia, which is confirmed by similar regularities obtained during a study of elevation and zone distribution of the xylomycocomplex of Transbaikalia.