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Fluctuations in environmental parameters are increasingly being recognized as essential features of any habitat. The quantification of whether environmental fluctuations are prevalently predictable or unpredictable is remarkably relevant to understanding the evolutionary responses of organisms. However, when characterizing the relevant features of natural habitats, ecologists typically face two problems: (1) gathering long-term data and (2) handling the hard-won data. This paper takes advantage of the free access to long-term recordings of remote sensing data (27 years, Landsat TM/ETM+) to assess a set of environmental models for estimating environmental predictability. The case study included 20 Mediterranean saline ponds and lakes, and the focal variable was the water-surface area. This study first aimed to produce a method for accurately estimating the water-surface area from satellite images. Saline ponds can develop salt-crusted areas that make it difficult to distinguish between soil and water. This challenge was addressed using a novel pipeline that combines band ratio water indices and the short near-infrared band as a salt filter. The study then extracted the predictable and unpredictable components of variation in the water-surface area. Two different approaches, each showing variations in the parameters, were used to obtain the stochastic variation around a regular pattern with the objective of dissecting the effect of assumptions on predictability estimations. The first approach, which is based on Colwell’s predictability metrics, transforms the focal variable into a nominal one. The resulting discrete categories define the relevant variations in the water-surface area. In the second approach, we introduced General Additive Model (GAM) fitting as a new metric for quantifying predictability. Both approaches produced a wide range of predictability for the studied ponds. Some model assumptions–which are considered very different a priori–had minor effects, whereas others produced predictability estimations that showed some degree of divergence. We hypothesize that these diverging estimations of predictability reflect the effect of fluctuations on different types of organisms. The fluctuation analysis described in this manuscript is applicable to a wide variety of systems, including both aquatic and non-aquatic systems, and will be valuable for quantifying and characterizing predictability, which is essential within the expected global increase in the unpredictability of environmental fluctuations. We advocate that a priori information for organisms of interest should be used to select the most suitable metrics for estimating predictability, and we provide some guidelines for this approach. [ABSTRACT FROM AUTHOR]

Boosted Regression Trees (BRT) is one of the modelling techniques most recently applied to biodiversity conservation and it can be implemented with presence-only data through the generation of artificial absences (pseudo-absences). In this paper, three pseudo-absences generation techniques are compared, namely the generation of pseudo-absences within target-group background (TGB), testing both the weighted (WTGB) and unweighted (UTGB) scheme, and the generation at random (RDM), evaluating their performance and applicability in distribution modelling and species conservation. The choice of the target group fell on amphibians, because of their rapid decline worldwide and the frequent lack of guidelines for conservation strategies and regional-scale planning, which instead could be provided through an appropriate implementation of SDMs. Bufo bufo, Salamandrina perspicillata and Triturus carnifex were considered as target species, in order to perform our analysis with species having different ecological and distributional characteristics. The study area is the “Gran Sasso—Monti della Laga” National Park, which hosts 15 Natura 2000 sites and represents one of the most important biodiversity hotspots in Europe. Our results show that the model calibration ameliorates when using the target-group based pseudo-absences compared to the random ones, especially when applying the WTGB. Contrarily, model discrimination did not significantly vary in a consistent way among the three approaches with respect to the tree target species. Both WTGB and RDM clearly isolate the highly contributing variables, supplying many relevant indications for species conservation actions. Moreover, the assessment of pairwise variable interactions and their three-dimensional visualization further increase the amount of useful information for protected areas’ managers. Finally, we suggest the use of RDM as an admissible alternative when it is not possible to individuate a suitable set of species as a representative target-group from which the pseudo-absences can be generated. [ABSTRACT FROM AUTHOR]

Many populations collapse suddenly when reaching low densities even if they have abundant food conditions, a phenomenon known as an Allee effect. Such collapses can have disastrous consequences, for example, for loss of biodiversity. In this paper, we formulate a stage-structured consumer-resource biomass model in which adults only reproduce at the beginning of each growing season, and investigate the effect of an increasing stage-independent background mortality rate of the consumer. As the main difference with previously studied continuous-time models, seasonal reproduction can result in an Allee effect and consumer population collapses at high consumer mortality rate. However, unlike the mechanisms reported in the literature, in our model the Allee effect results from the time difference between the maturation of juveniles and the reproduction of adults. The timing of maturation plays a crucial role because it not only determines the body size of the individuals at maturation but also influences the duration of the period during which adults can invest in reproductive energy, which together determine the reproductive output at the end of the season. We suggest that there exists an optimal timing of maturation and that consumer persistence is promoted if individuals mature later in the season at a larger body size, rather than maturing early, despite high food availability supporting rapid growth. [ABSTRACT FROM AUTHOR]

North America’s Great Basin has long been of interest to biologists due to its high level of organismal endemicity throughout its endorheic watersheds. One example of such a group is the subfamily Empetricthyinae. In this paper, we analyzed the relationships of the Empetrichtyinae and assessed the validity of the subspecies designations given by Williams and Wilde within the group using concatenated phylogenetic tree estimation and species tree estimation. Samples from 19 populations were included covering the entire distribution of the three extant species of Empetricthyinae–Crenichthys nevadae, Crenichthys baileyi and Empetricthys latos. Three nuclear introns (S8 intron 4, S7 intron 1, and P0 intron 1) and one mitochondrial gene (Cytb) were sequenced for phylogenetic analysis. Using these sequences, we generated two separate hypotheses of the evolutionary relationships of Empetrichtyinae- one based on the mitochondrial data and one based on the nuclear data using Bayesian phylogenetics. Haplotype networks were also generated to look at the relationships of the populations within Empetrichthyinae. After comparing the two phylogenetic hypotheses, species trees were generated using *BEAST with the nuclear data to further test the validity of the subspecies within Empetrichthyinae. The mitochondrial analyses supported four lineages within C. baileyi and 2 within C. nevadae. The concatenated nuclear tree was more conserved, supporting one clade and an unresolved polytomy in both species. The species tree analysis supported the presence of two species within both C. baileyi and C. nevadae. Based on the results of these analyses, the subspecies designations of Williams and Wilde are not valid, rather a conservative approach suggests there are two species within C. nevadae and two species within C. baileyi. No structure was found for E. latos or the populations of Empetricthyinae. This study represents one of many demonstrating the invalidity of subspecies and their detriment to species identification, conservation, and understanding. [ABSTRACT FROM AUTHOR]

Marine Protected Areas (MPA) are important management tools shown to protect marine organisms, restore biomass, and increase fisheries yields. While MPAs have been successful in meeting these goals for many relatively sedentary species, highly mobile organisms may get few benefits from this type of spatial protection due to their frequent movement outside the protected area. The use of a large MPA can compensate for extensive movement, but testing this empirically is challenging, as it requires both large areas and sufficient time series to draw conclusions. To overcome this limitation, MPA models have been used to identify designs and predict potential outcomes, but these simulations are highly sensitive to the assumptions describing the organism’s movements. Due to recent improvements in computational simulations, it is now possible to include very complex movement assumptions in MPA models (e.g. Individual Based Model). These have renewed interest in MPA simulations, which implicitly assume that increasing the detail in fish movement overcomes the sensitivity to the movement assumptions. Nevertheless, a systematic comparison of the designs and outcomes obtained under different movement assumptions has not been done. In this paper, we use an individual based model, interconnected to population and fishing fleet models, to explore the value of increasing the detail of the movement assumptions using four scenarios of increasing behavioral complexity: a) random, diffusive movement, b) aggregations, c) aggregations that respond to environmental forcing (e.g. sea surface temperature), and d) aggregations that respond to environmental forcing and are transported by currents. We then compare these models to determine how the assumptions affect MPA design, and therefore the effective protection of the stocks. Our results show that the optimal MPA size to maximize fisheries benefits increases as movement complexity increases from ~10% for the diffusive assumption to ~30% when full environment forcing was used. We also found that in cases of limited understanding of the movement dynamics of a species, simplified assumptions can be used to provide a guide for the minimum MPA size needed to effectively protect the stock. However, using oversimplified assumptions can produce suboptimal designs and lead to a density underestimation of ca. 30%; therefore, the main value of detailed movement dynamics is to provide more reliable MPA design and predicted outcomes. Large MPAs can be effective in recovering overfished stocks, protect pelagic fish and provide significant increases in fisheries yields. Our models provide a means to empirically test this spatial management tool, which theoretical evidence consistently suggests as an effective alternative to managing highly mobile pelagic stocks. [ABSTRACT FROM AUTHOR]

Background: Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale—patterns of species richness and species vulnerability to climate change—are affected by the inputs used to model and project species distribution. Methods: We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. Results: The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. Conclusions: From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species. [ABSTRACT FROM AUTHOR]

The social functions of urbanized areas are highly dependent on and supported by the convenient access to public transportation systems, particularly for the less privileged populations who have restrained auto ownership. To accurately evaluate the public transit accessibility, it is critical to capture the spatiotemporal variation of transit services. This can be achieved by measuring the shortest paths or minimum travel time between origin-destination (OD) pairs at each time-of-day (e.g. every minute). In recent years, General Transit Feed Specification (GTFS) data has been gaining popularity for between-station travel time estimation due to its interoperability in spatiotemporal analytics. Many software packages, such as ArcGIS, have developed toolbox to enable the travel time estimation with GTFS. They perform reasonably well in calculating travel time between OD pairs for a specific time-of-day (e.g. 8:00 AM), yet can become computational inefficient and unpractical with the increase of data dimensions (e.g. all times-of-day and large network). In this paper, we introduce a new algorithm that is computationally elegant and mathematically efficient to address this issue. An open-source toolbox written in C++ is developed to implement the algorithm. We implemented the algorithm on City of St. George’s transit network to showcase the accessibility analysis enabled by the toolbox. The experimental evidence shows significant reduction on computational time. The proposed algorithm and toolbox presented is easily transferable to other transit networks to allow transit agencies and researchers perform high resolution transit performance analysis. [ABSTRACT FROM AUTHOR]

Since wind power is integrated into the thermal power operation system, dynamic economic emission dispatch (DEED) has become a new challenge due to its uncertain characteristics. This paper proposes an adaptive grid based multi-objective Cauchy differential evolution (AGB-MOCDE) for solving stochastic DEED with wind power uncertainty. To properly deal with wind power uncertainty, some scenarios are generated to simulate those possible situations by dividing the uncertainty domain into different intervals, the probability of each interval can be calculated using the cumulative distribution function, and a stochastic DEED model can be formulated under different scenarios. For enhancing the optimization efficiency, Cauchy mutation operation is utilized to improve differential evolution by adjusting the population diversity during the population evolution process, and an adaptive grid is constructed for retaining diversity distribution of Pareto front. With consideration of large number of generated scenarios, the reduction mechanism is carried out to decrease the scenarios number with covariance relationships, which can greatly decrease the computational complexity. Moreover, the constraint-handling technique is also utilized to deal with the system load balance while considering transmission loss among thermal units and wind farms, all the constraint limits can be satisfied under the permitted accuracy. After the proposed method is simulated on three test systems, the obtained results reveal that in comparison with other alternatives, the proposed AGB-MOCDE can optimize the DEED problem while handling all constraint limits, and the optimal scheme of stochastic DEED can decrease the conservation of interval optimization, which can provide a more valuable optimal scheme for real-world applications. [ABSTRACT FROM AUTHOR]

We present a comprehensive analysis of estimation of fisheries Maximum Sustainable Yield (MSY) reference points using an ecosystem model built for Mille Lacs Lake, the second largest lake within Minnesota, USA. Data from single-species modelling output, extensive annual sampling for species abundances, annual catch-survey, stomach-content analysis for predatory-prey interactions, and expert opinions were brought together within the framework of an Ecopath with Ecosim (EwE) ecosystem model. An increase in the lake water temperature was observed in the last few decades; therefore, we also incorporated a temperature forcing function in the EwE model to capture the influences of changing temperature on the species composition and food web. The EwE model was fitted to abundance and catch time-series for the period 1985 to 2006. Using the ecosystem model, we estimated reference points for most of the fished species in the lake at single-species as well as ecosystem levels with and without considering the influence of temperature change; therefore, our analysis investigated the trophic and temperature effects on the reference points. The paper concludes that reference points such as MSY are not stationary, but change when (1) environmental conditions alter species productivity and (2) fishing on predators alters the compensatory response of their prey. Thus, it is necessary for the management to re-estimate or re-evaluate the reference points when changes in environmental conditions and/or major shifts in species abundance or community structure are observed. [ABSTRACT FROM AUTHOR]

Biomass gasification stations are facing many hazard factors, therefore, it is necessary to make hazard assessment for them. In this study, a novel hazard assessment method called extended set pair analysis (ESPA) is proposed based on set pair analysis (SPA). However, the calculation of the connection degree (CD) requires the classification of hazard grades and their corresponding thresholds using SPA for the hazard assessment. In regard to the hazard assessment using ESPA, a novel calculation algorithm of the CD is worked out when hazard grades and their corresponding thresholds are unknown. Then the CD can be converted into Euclidean distance (ED) by a simple and concise calculation, and the hazard of each sample will be ranked based on the value of ED. In this paper, six biomass gasification stations are introduced to make hazard assessment using ESPA and general set pair analysis (GSPA), respectively. By the comparison of hazard assessment results obtained from ESPA and GSPA, the availability and validity of ESPA can be proved in the hazard assessment for biomass gasification stations. Meanwhile, the reasonability of ESPA is also justified by the sensitivity analysis of hazard assessment results obtained by ESPA and GSPA. [ABSTRACT FROM AUTHOR]

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance ( = 0.907, RMSEC = 0.425%, = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance ( = 0.888, RMSEC = 0.446%, = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area. [ABSTRACT FROM AUTHOR]

Even as we advance the frontiers of physics knowledge, our understanding of how this knowledge evolves remains at the descriptive levels of Popper and Kuhn. Using the American Physical Society (APS) publications data sets, we ask in this paper how new knowledge is built upon old knowledge. We do so by constructing year-to-year bibliographic coupling networks, and identify in them validated communities that represent different research fields. We then visualize their evolutionary relationships in the form of alluvial diagrams, and show how they remain intact through APS journal splits. Quantitatively, we see that most fields undergo weak Popperian mixing, and it is rare for a field to remain isolated/undergo strong mixing. The sizes of fields obey a simple linear growth with recombination. We can also reliably predict the merging between two fields, but not for the considerably more complex splitting. Finally, we report a case study of two fields that underwent repeated merging and splitting around 1995, and how these Kuhnian events are correlated with breakthroughs on Bose-Einstein condensation (BEC), quantum teleportation, and slow light. This impact showed up quantitatively in the citations of the BEC field as a larger proportion of references from during and shortly after these events. [ABSTRACT FROM AUTHOR]

Background: Zika virus transmission dynamics in urban environments follow a complex spatiotemporal pattern that appears unpredictable and barely related to high mosquito density areas. In this context, human activity patterns likely have a major role in Zika transmission dynamics. This paper examines the effect of host variability in the amount of time spent outdoors on Zika epidemiology in an urban environment. Methodology/Principal findings: First, we performed a survey on time spent outdoors by residents of Miami-Dade County, Florida. Second, we analyzed both the survey and previously published national data on outdoors time in the U.S. to provide estimates of the distribution of the time spent outdoors. Third, we performed a computational modeling evaluation of Zika transmission dynamics, based on the time spent outdoors by each person. Our analysis reveals a strong heterogeneity of the host population in terms of time spent outdoors–data are well captured by skewed gamma distributions. Our model-based evaluation shows that in a heterogeneous population, Zika would cause a lower number of infections than in a more homogenous host population (up to 4-fold differences), but, at the same time, the epidemic would spread much faster. We estimated that in highly heterogeneous host populations the timing of the implementation of vector control measures is the major factor for limiting the number of Zika infections. Conclusions/Significance: Our findings highlight the need of considering host variability in exposure time for managing mosquito-borne infections and call for the revision of the triggers for vector control strategies, which should integrate mosquito density data and human outdoor activity patterns in specific areas. [ABSTRACT FROM AUTHOR]

The objective of this work is to develop a simple methodology for high resolution crop suitability analysis under current and future climate, easily applicable and useful in Least Developed Countries. The approach addresses both regional planning in the context of climate change projections and pre-emptive short-term rural extension interventions based on same-year agricultural season forecasts, while implemented with off-the-shelf resources. The developed tools are applied operationally in a case-study developed in three regions of Guinea-Bissau and the obtained results, as well as the advantages and limitations of methods applied, are discussed. In this paper we show how a simple approach can easily generate information on climate vulnerability and how it can be operationally used in rural extension services. [ABSTRACT FROM AUTHOR]

The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017. [ABSTRACT FROM AUTHOR]

Value orientations used to explain or justify conservation have been rooted in arguments about how much and in what context to emphasize the intrinsic versus instrumental value of nature. Equally prominent are characterizations of beliefs known as the New Ecological Paradigm (NEP), often used to help explain pro-environmental behaviour. A recent alternative to these positions has been identified as ‘relational value’—broadly, values linking people and ecosystems via tangible and intangible relationships to nature as well as the principles, virtues and notions of a good life that may accompany these. This paper examines whether relational values are distinct from other value orientation and have potential to alleviate the intrinsic-instrumental debate. To test this possibility, we sought to operationalize the construct—relational values—by developing six relational statements. We ask: 1) Do the individual statements used to characterize relational values demonstrate internal coherence as either a single or multi-dimensional construct? 2) Do relational value statements (including those strongly stated) resonate with diverse populations? 3) Do people respond to relational value statements in a consistently different way than NEP scale statements? Data for this work is drawn from an online panel of residents of northeastern US (n = 400), as well as a sample of Costa Rican farmers (n = 253) and tourists in Costa Rica (n = 260). Results indicate relational values are distinct as a construct when compared to the NEP. [ABSTRACT FROM AUTHOR]

Background: Enhancing water provision services is a common target in forest restoration projects worldwide due to growing concerns over freshwater scarcity. However, whether or not forest cover expansion or restoration can improve water provision services is still unclear and highly disputed. Purpose: The goal of this review is to provide a balanced and impartial assessment of the impacts of forest restoration and forest cover expansion on water yields as informed by the scientific literature. Potential sources of bias on the results of papers published are also examined. Data sources: English, Spanish and Portuguese peer-review articles in Agricola, CAB Abstracts, ISI Web of Science, JSTOR, Google Scholar, and SciELO. Databases were searched through 2015. Search terms: Intervention terms included forest restoration, regeneration/regrowth, forest second-growth, forestation/afforestation, and forestry. Target terms included water yield/quantity, streamflow, discharge, channel runoff, and annual flow. Study selection and eligibility criteria: Articles were pre-selected based on key words in the title, abstract or text. Eligible articles addressed relevant interventions and targets and included quantitative information. Results: Most studies reported decreases in water yields following the intervention, while other hydrological benefits have been observed. However, relatively few studies focused specifically on forest restoration, especially with native species, and/or on projects done at large spatial or temporal scales. Information is especially limited for the humid tropics and subtropics. Conclusions and implications of key findings: While most studies reported a decrease in water yields, meta-analyses from a sub-set of studies suggest the potential influence of temporal and/or spatial scales on the outcomes of forest cover expansion or restoration projects. Given the many other benefits of forest restoration, improving our understanding of when and why forest restoration can lead to recovery of water yields is crucial to help improve positive outcomes and prevent unintended consequences. Our study identifies the critical types of studies and associated measurements needed. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is predicted to have profound effects on species distributions over the coming decades. In this paper, we used maximum entropy modelling (Maxent) to estimate the effects of projected changes in climate on extent of climatically-suitable habitat for two Nepenthes pitcher plant species in Borneo. The model results predicted an increase in area of climatically-suitable habitat for the lowland species Nepenthes rafflesiana by 2100; in contrast, the highland species Nepenthes tentaculata was predicted to undergo significant loss of climatically-suitable habitat over the same period. Based on the results of the models, we recommend that research be undertaken into practical mitigation strategies, as approximately two-thirds of Nepenthes are restricted to montane habitats. Highland species with narrow elevational ranges will be at particularly high risk, and investigation into possible mitigation strategies should be focused on them. [ABSTRACT FROM AUTHOR]

In highly urbanized area where traffic condition fluctuates constantly, transportation infrastructure is one of the major contributing factors to Emergency Medical Service (EMS) availability and patient outcome. In this paper, we assess the impact of traffic fluctuation to the EMS first response availability in urban area, by evaluating the k-minute coverage under 21 traffic scenarios. The set of traffic scenarios represents the time-of-day and day-of-week effects, and is generated by combining road link speed information from multiple historical speed databases. In addition to the k-minute area coverage calculation, the k-minute population coverage is also evaluated for every 100m by 100m grid that partitions the case study area of Seoul, South Korea. In the baseline case of traveling at the speed limit, both the area and population coverage reached nearly 100% when compared to the five-minute travel time national target. Employing the proposed LoST (Loss of Serviceability due to Traffic) index, which measures coverage reduction in percentage compared to the baseline case, we find that the citywide average LoST for area and population coverage are similar at 34.2% and 33.8%. However, district-wise analysis reveals that such reduction varies significantly by district, and the magnitude of area and population coverage reduction is not always proportional. We conclude that the effect of traffic variation is significant to successful urban EMS first response performance, and regional variation is evident among local districts. Complexity in the urban environment requires a more adaptive approach in public health resource management and EMS performance target determination. [ABSTRACT FROM AUTHOR]

Adaptive management involves learning-oriented decision making in the presence of uncertainty about the responses of a resource system to management. It is implemented through an iterative sequence of decision making, monitoring and assessment of system responses, and incorporating what is learned into future decision making. Decision making at each point is informed by a value or objective function, for example total harvest anticipated over some time frame. The value function expresses the value associated with decisions, and it is influenced by system status as updated through monitoring. Often, decision making follows shortly after a monitoring event. However, it is certainly possible for the cadence of decision making to differ from that of monitoring. In this paper we consider different combinations of annual and biennial decision making, along with annual and biennial monitoring. With biennial decision making decisions are changed only every other year; with biennial monitoring field data are collected only every other year. Different cadences of decision making combine with annual and biennial monitoring to define 4 scenarios. Under each scenario we describe optimal valuations for active and passive adaptive decision making. We highlight patterns in valuation among scenarios, depending on the occurrence of monitoring and decision making events. Differences between years are tied to the fact that every other year a new decision can be made no matter what the scenario, and state information is available to inform that decision. In the subsequent year, however, in 3 of the 4 scenarios either a decision is repeated or monitoring does not occur (or both). There are substantive differences in optimal values among the scenarios, as well as the optimal policies producing those values. Especially noteworthy is the influence of monitoring cadence on valuation in some years. We highlight patterns in policy and valuation among the scenarios, and discuss management implications and extensions. [ABSTRACT FROM AUTHOR]

Modern cities are engines of production, innovation, and growth. However, urbanization also increases both local and global pollution from household consumption and firms’ production. Do emissions change proportionately to city size or does pollution tend to outpace or lag urbanization? Do emissions scale differently with population versus economic growth or are emissions, population, and economic growth inextricably linked? How are the scaling relationships between emissions, population, and economic growth affected by environmental regulation? This paper examines the link between urbanization, economic growth and pollution using data from Metropolitan Statistical Areas (MSAs) in the United States between 1999 and 2011. We find that the emissions of local air pollution in these MSAs scale according to a ¾ power law with both population size and gross domestic product (GDP). However, the monetary damages from these local emissions scale linearly with both population and GDP. Counties that have previously been out of attainment with the local air quality standards set by the Clean Air Act show an entirely different relationship: local emissions scale according to the square root of population, while the monetary damages from local air pollution follow a 2/3rds power law with population. Counties out of attainment are subject to more stringent emission controls; we argue based on this that enforcement of the Clean Air Act induces sublinear scaling between emissions, damages, and city size. In contrast, we find that metropolitan GDP scales super-linearly with population in all MSAs regardless of attainment status. Summarizing, our findings suggest that environmental policy limits the adverse effects of urbanization without interfering with the productivity benefits that manifest in cities. [ABSTRACT FROM AUTHOR]

The gut microbiome is highly variable among individuals, largely due to differences in host lifestyle and physiology. However, little is known about the underlying processes or rules that shape the complex microbial community. In this paper, we show that the cumulative relative abundance distribution (CRAD) of microbial species can be approximated by a power law function, and found that the power exponent of CRADs generated from 16S rRNA gene and metagenomic data for normal gut microbiomes of humans and mice was similar consistently with ∼0.9. A similarly robust power exponent was observed in CRADs of gut microbiomes during dietary interventions and several diseases. However, the power exponent was found to be ∼0.6 in CRADs from gut microbiomes characterized by lower species richness, such as those of human infants and the small intestine of mice. In addition, the CRAD of gut microbiomes of mice treated with antibiotics differed slightly from those of infants and the small intestines of mice. Based on these observations, in addition to data on the spatial distribution of microbes in the digestive tract, we developed a 3-dimensional mathematical model of microbial proliferation that reproduced the experimentally observed CRAD patterns. Our model indicated that the CRAD may be determined by the ratio of emerging to pre-existing species during non-uniform spatially competitive proliferation, independent of species composition. [ABSTRACT FROM AUTHOR]

Shortwave solar radiation is an important component of the surface energy balance and provides the principal source of energy for terrestrial ecosystems. This paper presents a machine learning approach in the form of a random forest (RF) model for estimating daily downward solar radiation flux at the land surface over complex terrain using MODIS (MODerate Resolution Imaging Spectroradiometer) remote sensing data. The model-building technique makes use of a unique network of 16 solar flux measurements in the semi-arid Reynolds Creek Experimental Watershed and Critical Zone Observatory, in southwest Idaho, USA. Based on a composite RF model built on daily observations from all 16 sites in the watershed, the model simulation of downward solar radiation matches well with the observation data (r2 = 0.96). To evaluate model performance, RF models were built from 12 of 16 sites selected at random and validated against the observations at the remaining four sites. Overall root mean square errors (RMSE), bias, and mean absolute error (MAE) are small (range: 37.17 W/m2-81.27 W/m2, -48.31 W/m2-15.67 W/m2, and 26.56 W/m2-63.77 W/m2, respectively). When extrapolated to the entire watershed, spatiotemporal patterns of solar flux are largely consistent with expected trends in this watershed. We also explored significant predictors of downward solar flux in order to reveal important properties and processes controlling downward solar radiation. Based on the composite RF model built on all 16 sites, the three most important predictors to estimate downward solar radiation include the black sky albedo (BSA) near infrared band (0.858 μm), BSA visible band (0.3–0.7 μm), and clear day coverage. This study has important implications for improving the ability to derive downward solar radiation through a fusion of multiple remote sensing datasets and can potentially capture spatiotemporally varying trends in solar radiation that is useful for land surface hydrologic and terrestrial ecosystem modeling. [ABSTRACT FROM AUTHOR]

With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis. [ABSTRACT FROM AUTHOR]

Reasoning with inconsistencies is an important issue for Semantic Web as imperfect information is unavoidable in real applications. For this, different paraconsistent approaches, due to their capacity to draw as nontrivial conclusions by tolerating inconsistencies, have been proposed to reason with inconsistent description logic knowledge bases. However, existing paraconsistent approaches are often criticized for being too skeptical. To this end, this paper presents a non-monotonic paraconsistent version of description logic reasoning, called minimally inconsistent reasoning, where inconsistencies tolerated in the reasoning are minimized so that more reasonable conclusions can be inferred. Some desirable properties are studied, which shows that the new semantics inherits advantages of both non-monotonic reasoning and paraconsistent reasoning. A complete and sound tableau-based algorithm, called multi-valued tableaux, is developed to capture the minimally inconsistent reasoning. In fact, the tableaux algorithm is designed, as a framework for multi-valued DL, to allow for different underlying paraconsistent semantics, with the mere difference in the clash conditions. Finally, the complexity of minimally inconsistent description logic reasoning is shown on the same level as the (classical) description logic reasoning. [ABSTRACT FROM AUTHOR]

This paper presents a survey on a system that uses digital image processing techniques to identify anthracnose and powdery mildew diseases of sandalwood from digital images. Our main objective is researching the most suitable identification technology for the anthracnose and powdery mildew diseases of the sandalwood leaf, which provides algorithmic support for the real-time machine judgment of the health status and disease level of sandalwood. We conducted real-time monitoring of Hainan sandalwood leaves with varying severity levels of anthracnose and powdery mildew beginning in March 2014. We used image segmentation, feature extraction and digital image classification and recognition technology to carry out a comparative experimental study for the image analysis of powdery mildew, anthracnose disease and healthy leaves in the field. Performing the actual test for a large number of diseased leaves pointed to three conclusions: (1) Distinguishing effects of BP (Back Propagation) neural network method, in all kinds of classical methods, for sandalwood leaf anthracnose and powdery mildew disease are relatively good; the size of the lesion areas were closest to the actual. (2) The differences between two diseases can be shown well by the shape feature, color feature and texture feature of the disease image. (3) Identifying and diagnosing the diseased leaves have ideal results by SVM, which is based on radial basis kernel function. The identification rate of the anthracnose and healthy leaves was 92% respectively, and that of powdery mildew was 84%. Disease identification technology lays the foundation for remote monitoring disease diagnosis, preparing for remote transmission of the disease images, which is a very good guide and reference for further research of the disease identification and diagnosis system in sandalwood and other species of trees. [ABSTRACT FROM AUTHOR]

Selection of value chain strategies by development practitioners and value chain participants themselves has been restricted to preset types of upgrading. This paper argues for an extension of the range of strategy solutions to value chains. An empirical application identifies successful strategies for honey value chains in Brazil for 2015–2020. Strategy and performance indicators were selected using the value chain Structure-Conduct-Performance (SCP) framework. Experts’ opinion was elicited in a Delphi for business scenarios, and adaptive conjoint analysis was used to identify strategies for increasing production growth and local value-added. This study identifies important strategies beyond upgrading typologies, and finds that important strategies differ by performance goal and scenario. The value chain SCP allows searching for promising strategies towards performance–the “better deal”–in an integrated way. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this paper is to provide a new method for estimating crash rate and severity simultaneously. Methods: This study explores a Heckman selection model of the crash rate and severity simultaneously at different levels and a two-step procedure is used to investigate the crash rate and severity levels. The first step uses a probit regression model to determine the sample selection process, and the second step develops a multiple regression model to simultaneously evaluate the crash rate and severity for slight injury/kill or serious injury (KSI), respectively. The model uses 555 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during two years. Results: The results of the proposed two-step Heckman selection model illustrate the necessity of different crash rates for different crash severity levels. Conclusions: A comparison with the existing approaches suggests that the Heckman selection model offers an efficient and convenient alternative method for evaluating the safety performance at signalized intersections. [ABSTRACT FROM AUTHOR]

A new head pose estimation technique based on Random Forest (RF) and texture features for facial image analysis using a monocular camera is proposed in this paper, especially about how to efficiently combine the random forest and the features. In the proposed technique a randomized tree with useful attributes is trained to improve estimation accuracy and tolerance of occlusions and illumination. Specifically, a number of features including Multi-scale Block Local Block Pattern (MB-LBP) are extracted from an image, and random features such as the MB-LBP scale parameters, a block coordinate, and a layer of an image pyramid in the feature pool are used for training the tree. The randomized tree aims to maximize the information gain at each node while random samples traverse the nodes in the tree. To this aim, a split function considering the uniform property of the LBP feature is developed to move sample blocks to the left or the right children nodes. The trees are independently trained with random inputs, yet they are grouped to form a random forest so that the results collected from the trees are used for make the final decision. Precisely, we use a Maximum-A-Posteriori criterion in the decision. It is demonstrated with experimental results that the proposed technique provides significantly enhanced classification performance in the head pose estimation in various conditions of illumination, poses, expressions, and facial occlusions. [ABSTRACT FROM AUTHOR]

Impact of changes in land cover and land use on hydrological service of tropical watersheds is one of the focal research tropics in both hydrology and Land Cover Land Use Changes (LCLUC). Land fragmentation is an important feature of LCLUC, however, its impact on hydrological service of tropical watershed is unclear despite a few theoretical frameworks. In this paper, we described a simulation study of eight tropical watersheds in Puerto Rico using the Soil Water Assessment Tool. Annual average stream discharge was derived according to the simulations with the land cover maps in 1977, 1991, and 2000. Annual big stream discharge with risks of flooding and severe soil erosion was computed as the sum of daily discharge greater than 95th percentile. The impacts of changes in land cover and fragmentation represented by perimeter-to-area ratio of land patches on annual average and big discharges were further analyzed by means of the linear mixed-effects model. Most mountainous watersheds were characterized by reforestation in 1977–1991 but slight deforestation in 1991–2000. Forest perimeter-to-area ratio was significantly correlated with covers of forest (correlation coefficient of -0.97), pasture (0.94), and urban (0.95). Thus forest fragmentation was reduced by reforestation but increased by deforestation. The annual average and big discharges were significantly reduced by forest cover and forest perimeter-to-area ratio. The enhanced edge effect by forest fragmentation may have incurred more effective interception of the subsurface flow by forest root system, and promoted forest transpiration, thus reduced stream flows. Land cover change plays more important roles in regulating the big discharges than altering the annual average discharges. Due to the negative correlation between forest cover and fragmentation, the decreased forest fragmentation accompanied with reforestation offsets the impact of reforestation on lessening freshwater supply and flooding risk. [ABSTRACT FROM AUTHOR]

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction. [ABSTRACT FROM AUTHOR]

Population growth and climate change challenge our food and farming systems and provide arguments for an increased intensification of agriculture. A promising option is eco-functional intensification through organic farming, an approach based on using and enhancing internal natural resources and processes to secure and improve agricultural productivity, while minimizing negative environmental impacts. In this concept an active soil microbiota plays an important role for various soil based ecosystem services such as nutrient cycling, erosion control and pest and disease regulation. Several studies have reported a positive effect of organic farming on soil health and quality including microbial community traits. However, so far no systematic quantification of whether organic farming systems comprise larger and more active soil microbial communities compared to conventional farming systems was performed on a global scale. Therefore, we conducted a meta-analysis on current literature to quantify possible differences in key indicators for soil microbial abundance and activity in organic and conventional cropping systems. All together we integrated data from 56 mainly peer-reviewed papers into our analysis, including 149 pairwise comparisons originating from different climatic zones and experimental duration ranging from 3 to more than 100 years. Overall, we found that organic systems had 32% to 84% greater microbial biomass carbon, microbial biomass nitrogen, total phospholipid fatty-acids, and dehydrogenase, urease and protease activities than conventional systems. Exclusively the metabolic quotient as an indicator for stresses on microbial communities remained unaffected by the farming systems. Categorical subgroup analysis revealed that crop rotation, the inclusion of legumes in the crop rotation and organic inputs are important farming practices affecting soil microbial community size and activity. Furthermore, we show that differences in microbial size and activity between organic and conventional farming systems vary as a function of land use (arable, orchards, and grassland), plant life cycle (annual and perennial) and climatic zone. In summary, this study shows that overall organic farming enhances total microbial abundance and activity in agricultural soils on a global scale. [ABSTRACT FROM AUTHOR]

The cotton mealybug, Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae), is a serious invasive species that significantly damages plants of approximately 60 families around the world. It is originally from North America and has also been introduced to other continents. Our goals were to create a current and future potential global distribution map for this pest under climate change with MaxEnt software. We tested the hypothesis of niche conservatism for P. solenopsis by comparing its native niche in North America to its invasive niches on other continents using Principal components analyses (PCA) in R. The potentially suitable habitat for P. solenopsis in its native and non-native ranges is presented in the present paper. The results suggested that the mean temperature of the wettest quarter and the mean temperature of the driest quarter are the most important environmental variables determining the potential distribution of P. solenopsis. We found strong evidence for niche shifts in the realized climatic niche of this pest in South America and Australia due to niche unfilling; however, a niche shift in the realized climatic niche of this pest in Eurasian owing to niche expansion. [ABSTRACT FROM AUTHOR]

Environmental impact assessment (EIA) is used globally to manage the impacts of development projects on the environment, so there is an imperative to demonstrate that it can effectively identify risky projects. However, despite the widespread use of quantitative predictive risk models in areas such as toxicology, ecosystem modelling and water quality, the use of predictive risk tools to assess the overall expected environmental impacts of major construction and development proposals is comparatively rare. A risk-based approach has many potential advantages, including improved prediction and attribution of cause and effect; sensitivity analysis; continual learning; and optimal resource allocation. In this paper we investigate the feasibility of using a Bayesian belief network (BBN) to quantify the likelihood and consequence of non-compliance of new projects based on the occurrence probabilities of a set of expert-defined features. The BBN incorporates expert knowledge and continually improves its predictions based on new data as it is collected. We use simulation to explore the trade-off between the number of data points and the prediction accuracy of the BBN, and find that the BBN could predict risk with 90% accuracy using approximately 1000 data points. Although a further pilot test with real project data is required, our results suggest that a BBN is a promising method to monitor overall risks posed by development within an existing EIA process given a modest investment in data collection. [ABSTRACT FROM AUTHOR]

The Soviet Union played the leading role in fish introductions in Eurasia. However, only 3% of all introductions prior to 1978 gave a commercial benefit. One of the noteworthy examples appears to be the Sevan trout (Salmo ischchan Kessler, 1877)—an endemic salmonid of Lake Sevan in Armenia. This species has been introduced to Kirghizstan, Kazakhstan, and Uzbekistan, however, only the Kirghiz population has persisted in relatively high numbers. In this paper we provide the first extensive molecular study of S. ischchan using samples from the native population from Lake Sevan and three hatcheries in Armenia, as well as from the population introduced to Lake Issyk Kul in Kirghizstan. The Kirghiz population has been isolated since the introductions took place in 1930 and 1936. Our results, based on 11 nuclear microsatellites and a 905 bp fragment of the mitochondrial control region suggest that hatcheries have maintained genetic variability by way of ongoing translocations of individuals from Lake Sevan. Simultaneously, significant Garza-Williamson M-values suggest that bottlenecks could have reduced the genetic variability of the wild populations in the past. This hypothesis is supported by historical data, indicating highly manipulated water-level regulations and poaching as two main factors that dramatically impact fish abundance in the lake. On the other hand, a similar situation has been observed in Kirghizstan, but this population likely rebounded from small population size faster than the other populations examined. The Kirghiz population is significantly genetically differentiated from the other groups and have morphological features and biological attributes not observed in the source population. Genetic data imply that the effective population size in the native population is lower than that found in the introduced population, suggesting that some active protection of the Lake Sevan population may be needed urgently. [ABSTRACT FROM AUTHOR]

In this paper, a statistically-based experimental design with response surface methodology (RSM) was employed to examine the effects of functional conditions on the photoelectrocatalytic oxidation of landfill leachate using a Cu/N co-doped TiO2 (Ti) electrode. The experimental design method was applied to response surface modeling and the optimization of the operational parameters of the photoelectro-catalytic degradation of landfill leachate using TiO2 as a photo-anode. The variables considered were the initial chemical oxygen demand (COD) concentration, pH and the potential bias. Two dependent parameters were either directly measured or calculated as responses: chemical oxygen demand (COD) removal and total organic carbon (TOC) removal. The results of this investigation reveal that the optimum conditions are an initial pH of 10.0, 4377.98mgL-1 initial COD concentration and 25.0 V of potential bias. The model predictions and the test data were in satisfactory agreement. COD and TOC removals of 67% and 82.5%, respectively, were demonstrated. Under the optimal conditions, GC/MS showed 73 organic micro-pollutants in the raw landfill leachate which included hydrocarbons, aromatic compounds and esters. After the landfill leachate treatment processes, 38 organic micro-pollutants disappeared completely in the photoelectrocatalytic process. [ABSTRACT FROM AUTHOR]

The increasing availability of digitized biodiversity data worldwide, provided by an increasing number of institutions and researchers, and the growing use of those data for a variety of purposes have raised concerns related to the "fitness for use" of such data and the impact of data quality (DQ) on the outcomes of analyses, reports, and decisions. A consistent approach to assess and manage data quality is currently critical for biodiversity data users. However, achieving this goal has been particularly challenging because of idiosyncrasies inherent in the concept of quality. DQ assessment and management cannot be performed if we have not clearly established the quality needs from a data user’s standpoint. This paper defines a formal conceptual framework to support the biodiversity informatics community allowing for the description of the meaning of "fitness for use" from a data user’s perspective in a common and standardized manner. This proposed framework defines nine concepts organized into three classes: DQ Needs, DQ Solutions and DQ Report. The framework is intended to formalize human thinking into well-defined components to make it possible to share and reuse concepts of DQ needs, solutions and reports in a common way among user communities. With this framework, we establish a common ground for the collaborative development of solutions for DQ assessment and management based on data fitness for use principles. To validate the framework, we present a proof of concept based on a case study at the Museum of Comparative Zoology of Harvard University. In future work, we will use the framework to engage the biodiversity informatics community to formalize and share DQ profiles related to DQ needs across the community. [ABSTRACT FROM AUTHOR]

This article builds on the premise that human consumption of goods, food and transport are the ultimate drivers of climate change. However, the nature of the climate change problem (well described as a tragedy of the commons) makes it difficult for individuals to recognise their personal duty to implement behavioural changes to reduce greenhouse gas emissions. Consequently, this article aims to analyse the climate change issue from a human-scale perspective, in which each of us has a clearly defined personal quota of CO2 emissions that limits our activity and there is a finite time during which CO2 emissions must be eliminated to achieve the “well below 2°C” warming limit set by the Paris Agreement of 2015 (COP21). Thus, this work’s primary contribution is to connect an equal per capita fairness approach to a global carbon budget, linking personal levels with planetary levels. Here, we show that a personal quota of 5.0 tons of CO2 yr-1 p-1 is a representative value for both past and future emissions; for this level of a constant per-capita emissions and without considering any mitigation, the global accumulated emissions compatible with the “well below 2°C” and 2°C targets will be exhausted by 2030 and 2050, respectively. These are references years that provide an order of magnitude of the time that is left to reverse the global warming trend. More realistic scenarios that consider a smooth transition toward a zero-emission world show that the global accumulated emissions compatible with the “well below 2°C” and 2°C targets will be exhausted by 2040 and 2080, respectively. Implications of this paper include a return to personal responsibility following equity principles among individuals, and a definition of boundaries to the personal emissions of CO2. [ABSTRACT FROM AUTHOR]

The study of the lithic assemblages of two French sites, the Bau de l’Aubesier and Payre, contributes new knowledge of the earliest Neanderthal techno-cultural variability. In this paper we present the results of a detailed technological analysis of Early Middle Palaeolithic lithic assemblages of MIS 8 and 7 age from the two sites, which are located on opposite sides of the Rhône Valley in the south-east of France. The MIS 9–7 period is considered in Europe to be a time of new behaviours, especially concerning lithic strategies. The shift from the Lower Palaeolithic to the Early Middle Palaeolithic is “classically” defined by an increase in the number of core technologies, including standardized ones, which are stabilized in the full Middle Palaeolithic (MIS 5–3), associated with the decline of the “Acheulean” biface. Applying a common technological approach to the analysis of the two assemblages highlights their technological variability with respect to reduction systems and end products. Differences between Payre and the Bau de l’Aubesier concerning raw material procurement and faunal exploitation only partially explain this multifaceted technological variability, which in our opinion also reflects the existence of distinct technological strategies within the same restricted geographic area, which are related to distinct traditions, site uses, and/or as yet unknown parameters. [ABSTRACT FROM AUTHOR]

Dempster-Shafer evidence theory has been widely used in various applications. However, to solve the problem of counter-intuitive outcomes by using classical Dempster-Shafer combination rule is still an open issue while fusing the conflicting evidences. Many approaches based on discounted evidence and weighted average evidence have been investigated and have made significant improvements. Nevertheless, all of these approaches have inherent flaws. In this paper, a new weighting factor is proposed to address this problem. First, a modified dissimilarity measurement is proposed which is characterized by both distance and conflict between evidences. Second, a measurement of information volume of each evidence based on Deng entropy is introduced. Then two kinds of weight derived from aforementioned measurement are combined to obtain a new weighting factor and a weighted average method based on the new weighting factor is proposed. Numerical examples are used to illustrate the validity and effectiveness of the proposed method. In the end, the new method is applied to a real-life application of river water quality monitoring, which effectively identify the major land use activities contributing to river pollution. [ABSTRACT FROM AUTHOR]

This paper concerns the changes occurred over both decennial and millennial spans of time in a sponge assemblage present in coralligenous biogenic build-ups growing at 15 m depth in the Ligurian Sea (Western Mediterranean). The comparison of the sponge diversity after a time interval of about 40 years (1973–2014) showed a significant reduction in species richness (about 45%). This decrease affected mainly the massive/erect sponges, and in particular the subclass Keratosa, with a species loss of 67%, while the encrusting and cavity dwelling sponges lost the 36% and 50%, respectively. The boring sponges lost only one species (25%). This changing pattern suggested that the inner habitat of the bioconstructions was less affected by the variations of the environmental conditions or by the human pressures which, on the contrary, strongly affected the species living on the surface of the biogenic build-ups. Five cores extracted from the bioherms, dating back to 3500 YBP, allowed to analyse the siliceous spicules remained trapped in them in order to obtain taxonomic information. Changes at generic level in diversity and abundance were observed at 500/250-years intervals, ranging between 19 and 33 genera. The number of genera showed a sharp decrease since 3500–3000 to 3000–2500 YBP. After this period, the genera regularly increased until 1500–1250 YBP, from when they progressively decreased until 1000–500 YBP. Tentatively, these changes could be related to the different climatic periods that followed one another in the Mediterranean area within the considered time span. The recent depletion in sponge richness recorded in the Ligurian coralligenous can be considered relevant. In fact, the analysis of the spicules indicated that the sponges living in these coralligenous habitats remained enough stable during 3000 years, but could have lost a significant part of their biodiversity in the last decades, coinciding with a series of warming episodes. [ABSTRACT FROM AUTHOR]

The dynamic relationship between reefs and the people who utilize them at a subsistence level is poorly understood. This paper characterizes atoll-scale patterns in shallow coral reef habitat and fish community structure, and correlates these with environmental characteristics and anthropogenic factors, critical to conservation efforts for the reefs and the people who depend on them. Hierarchical clustering analyses by site for benthic composition and fish community resulted in the same 3 major clusters: cluster 1–oceanic (close proximity to deep water) and uninhabited (low human impact); cluster 2–oceanic and inhabited (high human impact); and cluster 3–lagoonal (facing the inside of the lagoon) and inhabited (highest human impact). Distance from village, reef exposure to deep water and human population size had the greatest effect in predicting the fish and benthic community structure. Our study demonstrates a strong association between benthic and fish community structure and human use across the Ulithi Atoll (Yap State, Federated States of Micronesia) and confirms a pattern observed by local people that an ‘opportunistic’ scleractinian coral (Montipora sp.) is associated with more highly impacted reefs. Our findings suggest that small human populations (subsistence fishing) can nevertheless have considerable ecological impacts on reefs due, in part, to changes in fishing practices rather than overfishing per se, as well as larger global trends. Findings from this work can assist in building local capacity to manage reef resources across an atoll-wide scale, and illustrates the importance of anthropogenic impact even in small communities. [ABSTRACT FROM AUTHOR]

‘Living in Harmony with Nature’ is a 2050 vision put forth by the Convention on Biological Diversity (CBD) which takes into consideration culture and locality of perceptions of nature and aspirations for its use. Considering that 54% of the world population lives in cities, where nature has been decimated, the role of rural communities, living within or in proximity of natural and semi natural areas, will effectively influence the fate of the ‘remaining’ nature while they engage in local development. Reconciling between growth and development on the one hand, and nature conservation on the other, necessitates an understanding of how rural communities, especially the youth, imprint their own ideas on landscapes and develop ownership over natural spaces. In order to extend the understanding of how harmony with nature is perceived in different parts of the world, this paper presents the findings of primary research involving a group of young people who live in rural areas in Lebanon, a country in the Arab Middle East. Participatory research based on photovoice methodology was conducted with 77 young people aged 7–16 and residing in five rural villages located in different parts of Lebanon. Photographs taken by participants indicated that for many, nature was not perceived literally i.e. woodlands, forests, plants, animals, etc…. Instead, the participants saw nature as part of agriculture and local culture. Nature was also seen as symbolic expression of the participants’ inner state of mind. Narratives written to explain the photographs shed light on the perception of harmony with nature which focused on positive family experiences and relayed personal emotions, abstract, and holistic yet functional view of nature. Another recurring theme that emerged from photographs and narratives was the role of family members, in particular grandparents, as having a strong influence on the positive perceptions of nature. [ABSTRACT FROM AUTHOR]

This paper proposes a reliable facility location design model under imperfect information with site-dependent disruptions; i.e., each facility is subject to a unique disruption probability that varies across the space. In the imperfect information contexts, customers adopt a realistic “trial-and-error” strategy to visit facilities; i.e., they visit a number of pre-assigned facilities sequentially until they arrive at the first operational facility or give up looking for the service. This proposed model aims to balance initial facility investment and expected long-term operational cost by finding the optimal facility locations. A nonlinear integer programming model is proposed to describe this problem. We apply a linearization technique to reduce the difficulty of solving the proposed model. A number of problem instances are studied to illustrate the performance of the proposed model. The results indicate that our proposed model can reveal a number of interesting insights into the facility location design with site-dependent disruptions, including the benefit of backup facilities and system robustness against variation of the loss-of-service penalty. [ABSTRACT FROM AUTHOR]

We compare the use of multi-criteria decision analysis (MCDA)–or more precisely, models used in multi-attribute value theory (MAVT)–to integrated assessment (IA) models for supporting long-term water supply planning in a small town case study in Switzerland. They are used to evaluate thirteen system scale water supply alternatives in four future scenarios regarding forty-four objectives, covering technical, social, environmental, and economic aspects. The alternatives encompass both conventional and unconventional solutions and differ regarding technical, spatial and organizational characteristics. This paper focuses on the impact assessment and final evaluation step of the structured MCDA decision support process. We analyze the performance of the alternatives for ten stakeholders. We demonstrate the implications of model assumptions by comparing two IA and three MAVT evaluation model layouts of different complexity. For this comparison, we focus on the validity (ranking stability), desirability (value), and distinguishability (value range) of the alternatives given the five model layouts. These layouts exclude or include stakeholder preferences and uncertainties. Even though all five led us to identify the same best alternatives, they did not produce identical rankings. We found that the MAVT-type models provide higher distinguishability and a more robust basis for discussion than the IA-type models. The needed complexity of the model, however, should be determined based on the intended use of the model within the decision support process. The best-performing alternatives had consistently strong performance for all stakeholders and future scenarios, whereas the current water supply system was outperformed in all evaluation layouts. The best-performing alternatives comprise proactive pipe rehabilitation, adapted firefighting provisions, and decentralized water storage and/or treatment. We present recommendations for possible ways of improving water supply planning in the case study and beyond. [ABSTRACT FROM AUTHOR]

Electronic maps (E-maps) provide people with convenience in real-world space. Although web map services can display maps on screens, a more important function is their ability to access geographical features. An E-map that is based on raster tiles is inferior to vector tiles in terms of interactive ability because vector maps provide a convenient and effective method to access and manipulate web map features. However, the critical issue regarding rendering tiled vector maps is that geographical features that are rendered in the form of map symbols via vector tiles may cause visual discontinuities, such as graphic conflicts and losses of data around the borders of tiles, which likely represent the main obstacles to exploring vector map tiles on the web. This paper proposes a tiled vector data model for geographical features in symbolized maps that considers the relationships among geographical features, symbol representations and map renderings. This model presents a method to tailor geographical features in terms of map symbols and ‘addition’ (join) operations on the following two levels: geographical features and map features. Thus, these maps can resolve the visual discontinuity problem based on the proposed model without weakening the interactivity of vector maps. The proposed model is validated by two map data sets, and the results demonstrate that the rendered (symbolized) web maps present smooth visual continuity. [ABSTRACT FROM AUTHOR]

Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results. [ABSTRACT FROM AUTHOR]

Insecticide usage in the United States is ubiquitous in urban, suburban, and rural environments. There is accumulating evidence that insecticides adversely affect non-target wildlife species, including birds, causing mortality, reproductive impairment, and indirect effects through loss of prey base, and the type and magnitude of such effects differs by chemical class, or mode of action. In evaluating data for an insecticide registration application and for registration review, scientists at the United States Environmental Protection Agency (USEPA) assess the fate of the insecticide and the risk the insecticide poses to the environment and non-target wildlife. Current USEPA risk assessments for pesticides generally rely on endpoints from laboratory based toxicity studies focused on groups of individuals and do not directly assess population-level endpoints. In this paper, we present a mechanistic model, which allows risk assessors to estimate the effects of insecticide exposure on the survival and seasonal productivity of birds known to forage in agricultural fields during their breeding season. This model relies on individual-based toxicity data and translates effects into endpoints meaningful at the population level (i.e., magnitude of mortality and reproductive impairment). The model was created from two existing USEPA avian risk assessment models, the Terrestrial Investigation Model (TIM v.3.0) and the Markov Chain Nest Productivity model (MCnest). The integrated TIM/MCnest model was used to assess the relative risk of 12 insecticides applied via aerial spray to control corn pests on a suite of 31 avian species known to forage in cornfields in agroecosystems of the Midwest, USA. We found extensive differences in risk to birds among insecticides, with chlorpyrifos and malathion (organophosphates) generally posing the greatest risk, and bifenthrin and λ-cyhalothrin (pyrethroids) posing the least risk. Comparative sensitivity analysis across the 31 species showed that ecological trait parameters related to the timing of breeding and reproductive output per nest attempt offered the greatest explanatory power for predicting the magnitude of risk. An important advantage of TIM/MCnest is that it allows risk assessors to rationally combine both acute (lethal) and chronic (reproductive) effects into a single unified measure of risk. [ABSTRACT FROM AUTHOR]