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。     

Conspecific Attraction is a Missing Component in Wildlife Habitat Modeling

Abstract Wildlife biologists use knowledge about wildlife-habitat relationships to create habitat models to predict species occurrence across a landscape. Researchers attribute limitations in predictive ability of a habitat model to data deficiencies, missing parameters, error introduced by specifications of the statistical model, and natural variation. Few wildlife biologists, however, have incorporated intra- and interspecific interactions (e.g., conspecific attraction, competition, predator-prey relationships) to increase predictive accuracy of habitat models. Based on our literature review and preliminary data analysis, conspecific attraction can be a primary factor influencing habitat selection in wildlife. Conspecific attraction can lead to clustered distributions of wildlife within available habitat, reducing the predictive ability of habitat models based on vegetative and geographic parameters alone. We suggest wildlife biologists consider incorporating a parameter in habitat models for the clustered distribution of individuals within available habitat and investigate the mechanisms leading to clustered distributions of species, especially conspecific attraction.     

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.     

质量管理工具在抗菌药物使用强度管理中的运用

目的 探讨质量管理工具在抗菌药物使用强度管理中的应用价值。方法 采用PDCA循环、追踪方法学、根本原因分析法、头脑风暴法、鱼骨图、决策树等管理工具,寻找医院抗菌药物使用强度管理中存在的问题并进行持续质量改进。结果 通过质量管理工具的应用,医院抗菌药物使用强度明显下降,并控制在每百人天40 DDDs以下。结论 质量管理工具应用于抗菌药物使用强度管理,能有效发现并解决问题,促进抗菌药物临床合理使用。     

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.     

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.     

Spatial and Temporal Pattern of Caddisfly Distribution at a Mesohabitat Scale in two Patagonian Mountain Streams Subjected to Pastoral Use

Substrate, flow type, nutrients, aquatic vegetation, organic matter, and caddisfly community structure were studied at two low order streams (Glyn and Nant y Fall) subjected to pastoral use in the Patagonian mountains. At both sites, we examined the effect of habitat type (boulder‐pebble with and without filamentous algae, cobble‐pebble, gravel‐sand, leaf‐pack, the submerged macrophytes Myriophyllum quitense and Isoetes savatieri) and season (high and low water period) on caddisfly assemblages. Benthic particulate organic matter (BPOM) ranged between 4.6 and 472 g m^–2, all allochtonous detrital fractions were significantly higher at leaf‐packs at Glyn, whereas M. quitense habitats supported more BPOM and macrophytes biomass at Nant y Fall. As expected, boulder‐pebble sustained higher Trichoptera richness than M. quitense and gravel‐sand, moreover all habitats showed higher density than M. quitense at Nant y Fall. According to our results at least nine caddisfly species exhibited some habitat preference with boulder‐pebble and cobble‐pebble the most selected habitat. These particular habitats sustained more than 68% of the total caddisfly species. Multidimensional scaling ordination highlighted differences in composition per habitat for both sites showing a clear distinction among depositional and erosional habitats. Substrate, flow type, detritus biomass were important predictors defining assemblages. Based upon our findings, those anthropogenic actions or stressors that change hydraulic as well substrate attributes in mountain streams such as stock trampling, dredging, clearing of riparian areas, will reduce caddisfly richness. These results are relevant for outline management and conservation biomonitoring and schemes in headwater Patagonian streams as well as other similar environments worldwide (© 2012 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

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.     

Adult Male Density Influences Juvenile Microhabitat Use in a Territorial Lizard

Habitat choice often has strong effects on performance and fitness. For many animals, optimal habitats differ across age or size classes, and individuals shift habitat use through ontogeny. Although many studies document ontogenetic habitat shifts for various taxa, most are observational and do not identify the causal factor of size‐specific habitat variation. Field observations of the brown anole lizard (Anolis sagrei) show that juveniles perch on shorter and thinner vegetation than adults. We hypothesized that this variation is due to adult males forcing smaller juveniles to less preferred habitat. To test this assertion, we manipulated adult male densities in mesh enclosures with artificial trees to examine the response of juvenile microhabitat choice. We found that adult male density had strong effects on juvenile perch height, perch width, and substrate use, suggesting that age‐class competition contributes to the observed ontogenetic differences in habitat choice. We also found that time of day significantly affected juvenile perch height and substrate use. In many cases, our results suggest that juveniles distance themselves from adults using different microhabitats from those used in our control ‘no‐adult’ treatment. However, these findings were often body size dependent and varied depending upon time of day. This study highlights the complexity of juvenile perching behavior and demonstrates the role of intraspecific interactions in shaping habitat use by juvenile animals.     

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.     

Culling Versus Density Effects in Management of a Deer Population

Abstract: Wildlife managers often manipulate hunting regulations to control deer populations. However, few empirical studies have examined the level of hunting effort (hunter-days) required to limit population growth and demographic effects through harvesting of females. Moreover, the relative importance of density effects on population growth has not been quantified. We reconstructed a sika deer [Cervus nippon] population over a period of 12 years (1990–2001) using age- and sex-specific harvest data. Using cohort analysis, we analyzed population dynamics, focusing on 1) the relationship between hunting effort and hunting-induced mortality rate, 2) relative contributions of hunting mortality and recruitment of yearlings to annual changes in population growth rate, and 3) annual variation in recruitment rate. Population size increased until 1998 and declined thereafter. The population growth rate changed more in response to annual changes in recruitment rate than hunting mortality rate. Temporal variation in recruitment rate was not controlled by birth rate alone; direct density dependence, intensities of hunting mortality for fawns, and for females (≥2 yr of age), which accounted for the fawn survival rate, were required as factors to explain temporal variation. Density effects on the recruitment rate were not strong enough to regulate the population within the study period; high hunting mortality, with intensive female harvesting, was necessary to prevent population growth. Hunting effort was a good predictor of the hunting mortality rate, and female harvest had a negative effect on the recruitment rate through fawn survival. We suggest that >3,500 hunter-days and prioritization of female harvesting are required to prevent increases in this deer population.