Coarse Spatial Scales Research Articles

Arctic Sea Ice Surface Roughness Estimated from Multi-Angular Reflectance Satellite Imagery

Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer meltponds, and affects ice albedo. We have developed a method for mapping sea ice surface roughness using angular reflectance data from the Multi-angle Imaging SpectroRadiometer (MISR) and lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). Using an empirical data modeling approach, we derived estimates of Arctic sea ice roughness ranging from centimeters to decimeters within the MISR 275-m pixel size. Using independent ATM data for validation, we find that histograms of lidar and multi-angular roughness values were nearly identical for areas with a roughness < 20 cm, but for rougher regions, the MISR-estimated roughness had a narrower range of values than the ATM data. The algorithm was able to accurately identify areas that transition between smooth and rough ice. Because of its coarser spatial scale, MISR-estimated roughness data have a variance about half that of ATM roughness data.

Linking photosynthesis and sun-induced fluorescence at sub-daily to seasonal scales

Due to its close link to the photosynthetic process, sun-induced chlorophyll fluorescence (F) opens new possibilities to study dynamics of photosynthetic light reactions and to quantify CO2 assimilation rates. Although recent studies show that F is linearly related to gross primary production (GPP) on coarse spatial and temporal scales, it is argued that this relationship may be mainly driven by seasonal changes in absorbed photochemical active radiation (APAR) and less by the plant light use efficiency (LUE).In this work a high-resolution spectrometer was used to continuously measure red and far-red fluorescence and different reflectance indices within a sugar beet field during the growing season in 2015. Diurnal and seasonal developments were compared to eddy covariance derived GPP.Additionally, part of the time series coincided with a heatwave. The induced drought stress allowed us to observe F and its relationship to GPP under changing environmental conditions during the seasonal cycle.Across the season a strong linear relationship between GPP and F760 was found. This relationship however, was mainly driven by changes in APAR and was strongly reduced under drought conditions. We could show that far-red fluorescence yield can explain 59% of the diurnal and 79% of the seasonal variance in the light use efficiency. However, an even stronger relationship between FY760 and the structural vegetation index MTVI2 was found, implying that FY760 is affected by seasonal structural changes of the canopy. Nevertheless, the seasonally de-trended FY760 and PRI show that they share strong interdependencies with seasonal and diurnal LUE, in particular under drought stress conditions.

A GIS based method for indexing the broad-leaved forest surfaces by their wildfire ignition probability and wildfire spreading capacity

This article presents a method useful for indexing the broad-leaved forest surfaces by their wildfire ignition probability and wildfire spreading capacity at a coarse spatial scale. The framework consists of three phases; inventory, analysis, and indexing. First, the study utilizes a multi-criteria inventory procedure investigating the existing broad-leaved forest areas of the landscape based on a variety of social, environmental, and physical parameters. Beyond the statistical inventory records, the research brings forward a division between ignition probability and spreading capacities of wildfire events during the analysis phase. At this stage, particular criteria figures out to have higher impact in either ignition or spreading phases of wildfire event. At the final phase, the model is aiming to generate indexing maps categorizing the broad-leaved forest surfaces by their Wildfire Ignition Probability Index and Wildfire Spread Capacity Index. Broad-leaved forest landscape patch as derived via CORINE Land Cover data, is converted into a raster data with a pixel size of 500 m (25 ha). The centroid of each pixel act as the reference point for all measurements during all phases of the study. The presented method is aimed to be of assistance in decision making and management processes of Disaster Risk Management and Fire Safety (DRMFS) agendas at landscape scale.

Cross-Scale Correlation between In Situ Measurements of Canopy Gap Fraction and Landsat-Derived Vegetation Indices with Implications for Monitoring the Seasonal Phenology in Tropical Forests Using MODIS Data

Deciduousness in dry tropical forests results in substantial seasonal changes to canopy gap fractions. The characterization of such structural properties over large areas is necessary for understanding energy and nutrient distribution within forest ecosystems. However, a spatial extrapolation of measurements from relatively few, spatially-concentrated field observations can yield estimated values that have questionable accuracy and precision at regional scales. This paper uses linear regression models to compare measurements of canopy gap fraction from in situ digital cover photography in the dry tropical forest of the Southern Yucatán, Mexico, to measurements of seasonal vegetation change based on three vegetation indices—the Normalized Difference Vegetation Index (NDVI), two-band Enhanced Vegetation Index (EVI2), and the Normalized Difference Water Index (NDWI)—derived from Landsat-7 ETM+ and Landsat-8 Operational Land Imager (OLI) data to gauge the ability of standardized combinations of multispectral reflectance data to accurately describe the intensity of deciduousness that occurs during the dry season. Discrete observations are compared, as well as spatially summarized values at coarser spatial scales. Model R2 values are greater at coarse spatial scales for all vegetation indices. Models of in situ measurements of gap fraction and Landsat NDWI normalized seasonal change exhibit stronger correlation than do models that feature NDVI or EVI2 (R² = 0.751 and Mean Absolute Error = 0.04 after aggregation, R² = 0.552 and MAE = 0.07 for observation-level data). Based on its comparatively strong correlation with field observations, NDWI is adapted to a Moderate Resolution Imaging Spectroradiometer (MODIS) time series and used for spatial extrapolation and the monitoring of canopy conditions. NDWI values derived from MODIS data are regressed against Tropical Rainforest Measuring Misson (TRMM) rainfall data over the period 2000–2011, and the regression results are compared to those of a prior study that used regression to explain the variation of a MODIS EVI using TRMM rainfall data. A MODIS NDWI time series reveals stronger correlation (R² = 0.48 in deciduous forests) with TRMM accumulated (three-month) rainfall data than a MODIS EVI time series. The results indicate that an NDWI time series can accurately describe a variability of canopy leaf abundance during the dry season and could be an alternative basis of long-term monitoring of season phenology in a dry tropical forest.

Effective bulk modulus of the pore fluid at patchy saturation

ABSTRACTWe explore a package of parallel porous layers, each filled with a different fluid. Assume that this package is sampled by an elastic wave with the wavelength much larger than the thickness of an individual layer. Also assume that the layers are hydraulically isolated from each other, meaning that the diffusion length is smaller than that of the individual layer. This assumption is relevant to a patchy saturation scenario. Suppose that we wish to conduct the fluid substitution operation on this package treated as a single porous elastic body. What is the effective bulk modulus of the pore fluid to be used in this operation that will result in the same elastic modulus as computed by Backus averaging the individual moduli of the layers? We address this question analytically by assuming that the porosity, dry frame, and the mineral matrix properties of the individual layers are the same for all layers. The only difference between the layers is the pore fluid. We find that the resulting effective bulk modulus of the fluid thus derived falls between the arithmetic and harmonic averages of the fluid bulk moduli in the layers. It can be approximated by a linear combination of these two bounds where the weights are 0.50 and 0.50 or 0.75 for the arithmetic average and 0.25 for the harmonic average, depending on the elastic moduli of the dry frame, the mineral, and the pore fluids. This solution also provides a relation between the effective bulk modulus of the pore fluid in the system under examination and water saturation to be used in the fluid substitution operation at a coarse spatial scale.

A computational model of shared fine-scale structure in the human connectome.

Variation in cortical connectivity profiles is typically modeled as having a coarse spatial scale parcellated into interconnected brain areas. We created a high-dimensional common model of the human connectome to search for fine-scale structure that is shared across brains. Projecting individual connectivity data into this new common model connectome accounts for substantially more variance in the human connectome than do previous models. This newly discovered shared structure is closely related to fine-scale distinctions in representations of information. These results reveal a shared fine-scale structure that is a major component of the human connectome that coexists with coarse-scale, areal structure. This shared fine-scale structure was not captured in previous models and was, therefore, inaccessible to analysis and study.

Reducing Uncertainties in Applying Remotely Sensed Land Use and Land Cover Maps in Land-Atmosphere Interaction: Identifying Change in Space and Time

Land use and land cover (LULC) data are a central component of most land-atmosphere interaction studies, but there are two common and highly problematic scale mismatches between LULC and climate data. First, in the spatial domain, researchers rarely consider the impact of scaling up fine-scale LULC data to match coarse-scale climate datasets. Second, in the temporal domain, climate data typically have sub-daily, daily, monthly, or annual resolution, but LULC datasets often have much coarser (e.g., decadal) resolution. We first explored the effect of three spatial scaling methods on correlations among LULC data and a land surface climatic variable, latent heat flux in China. Scaling by a fractional method preserved significant correlations among LULC data and latent heat flux at all three studied scales (0.5°, 1.0°, and 2.5°), whereas nearest-neighbor and majority-aggregation methods caused these correlations to diminish and even become statistically non-significant at coarser spatial scales (i.e., 2.5°). In the temporal domain, we identified fractional changes in croplands, forests, and grasslands in China using a recently developed and annually resolved time series of LULC maps from 1982 to 2012. Relative to common LULC change (LULCC) analyses conducted over two-time steps or several time periods, this annually resolved, 31-year time series of LULC maps enables robust interpretation of LULCC. Specifically, the annual resolution of these data enabled us to more precisely observe three key and statistically significant LULCC trends and transitions that could have consequential effects on land-atmosphere interaction: (1) decreasing grasslands to increasing croplands in the Northeast China plain and the Yellow river basin, (2) decreasing croplands to increasing forests in the Yangtze river basin, and (3) decreasing grasslands to increasing forests in Southwest China. Our study not only demonstrates the importance of using a fractional spatial rescaling method, but also illustrates the value of annually resolved LULC time series for detecting significant trends and transitions in LULCC, thus potentially facilitating a more robust use of remotely sensed data in land-atmosphere interaction studies.

In vivo quantification of plant starch reserves at micrometer resolution using X-ray microCT imaging and machine learning.

Starch is the primary energy storage molecule used by most terrestrial plants to fuel respiration and growth during periods of limited to no photosynthesis, and its depletion can drive plant mortality. Destructive techniques at coarse spatial scales exist to quantify starch, but these techniques face methodological challenges that can lead to uncertainty about the lability of tissue-specific starch pools and their role in plant survival. Here, we demonstrate how X-ray microcomputed tomography (microCT) and a machine learning algorithm can be coupled to quantify plant starch content invivo, repeatedly and nondestructively over time in grapevine stems (Vitis spp.). Starch content estimated for xylem axial and ray parenchyma cells from microCT images was correlated strongly with enzymatically measured bulk-tissue starch concentration on the same stems. After validating our machine learning algorithm, we then characterized the spatial distribution of starch concentration in living stems at micrometer resolution, and identified starch depletion in live plants under experimental conditions designed to halt photosynthesis and starch production, initiating the drawdown of stored starch pools. Using X-ray microCT technology for invivo starch monitoring should enable novel research directed at resolving the spatial and temporal patterns of starch accumulation and depletion in woody plant species.

Continental-Scale, Data-Driven Predictive Assessment of Eliminating the Vector-Borne Disease, Lymphatic Filariasis, in Sub-Saharan Africa by 2020

There are growing demands for predicting the prospects of achieving the global elimination of neglected tropical diseases as a result of the institution of large-scale nation-wide intervention programs by the WHO-set target year of 2020. Such predictions will be uncertain due to the impacts that spatial heterogeneity and scaling effects will have on parasite transmission processes, which will introduce significant aggregation errors into any attempt aiming to predict the outcomes of interventions at the broader spatial levels relevant to policy making. We describe a modeling platform that addresses this problem of upscaling from local settings to facilitate predictions at regional levels by the discovery and use of locality-specific transmission models, and we illustrate the utility of using this approach to evaluate the prospects for eliminating the vector-borne disease, lymphatic filariasis (LF), in sub-Saharan Africa by the WHO target year of 2020 using currently applied or newly proposed intervention strategies. We show how a computational platform that couples site-specific data discovery with model fitting and calibration can allow both learning of local LF transmission models and simulations of the impact of interventions that take a fuller account of the fine-scale heterogeneous transmission of this parasitic disease within endemic countries. We highlight how such a spatially hierarchical modeling tool that incorporates actual data regarding the roll-out of national drug treatment programs and spatial variability in infection patterns into the modeling process can produce more realistic predictions of timelines to LF elimination at coarse spatial scales, ranging from district to country to continental levels. Our results show that when locally applicable extinction thresholds are used, only three countries are likely to meet the goal of LF elimination by 2020 using currently applied mass drug treatments, and that switching to more intensive drug regimens, increasing the frequency of treatments, or switching to new triple drug regimens will be required if LF elimination is to be accelerated in Africa. The proportion of countries that would meet the goal of eliminating LF by 2020 may, however, reach up to 24/36 if the WHO 1% microfilaremia prevalence threshold is used and sequential mass drug deliveries are applied in countries. We have developed and applied a data-driven spatially hierarchical computational platform that uses the discovery of locally applicable transmission models in order to predict the prospects for eliminating the macroparasitic disease, LF, at the coarser country level in sub-Saharan Africa. We show that fine-scale spatial heterogeneity in local parasite transmission and extinction dynamics, as well as the exact nature of intervention roll-outs in countries, will impact the timelines to achieving national LF elimination on this continent.

Upscaling biodiversity: estimating the species–area relationship from small samples

AbstractThe challenge of biodiversity upscaling, estimating the species richness of a large area from scattered local surveys within it, has attracted increasing interest in recent years, producing a wide range of competing approaches. Such methods, if successful, could have important applications to multi‐scale biodiversity estimation and monitoring. Here we test 19 techniques using a high quality plant data set: the GB Countryside Survey 1999, detailed surveys of a stratified random sample of British landscapes. In addition to the full data set, a set of geographical and statistical subsets was created, allowing each method to be tested on multiple data sets with different characteristics. The predictions of the models were tested against the “true” species–area relationship for British plants, derived from contemporaneously surveyed national atlas data. This represents a far more ambitious test than is usually employed, requiring 5–10 orders of magnitude in upscaling. The methods differed greatly in their performance; while there are 2,326 focal plant taxa recorded in the focal region, up‐scaled species richness estimates ranged from 62 to 11,593. Several models provided reasonably reliable results across the 16 test data sets: the Shen and He and the Ulrich and Ollik models provided the most robust estimates of total species richness, with the former generally providing estimates within 10% of the true value. The methods tested proved less accurate at estimating the shape of the species–area relationship (SAR) as a whole; the best single method was Hui's Occupancy Rank Curve approach, which erred on average by <20%. A hybrid method combining a total species richness estimate (from the Shen and He model) with a downscaling approach (the Šizling model) proved more accurate in predicting the SAR (mean relative error 15.5%) than any of the pure upscaling approaches tested. There remains substantial room for improvement in upscaling methods, but our results suggest that several existing methods have a high potential for practical application to estimating species richness at coarse spatial scales. The methods should greatly facilitate biodiversity estimation in poorly studied taxa and regions, and the monitoring of biodiversity change at multiple spatial scales.

Exploring the small-scale spatial distribution of hypertension and its association to area deprivation based on health insurance claims in Northeastern Germany

BackgroundHypertension is one of the most frequently diagnosed chronic conditions in Germany. Targeted prevention strategies and allocation of general practitioners where they are needed most are necessary to prevent severe complications arising from high blood pressure. However, data on chronic diseases in Germany are mostly available through survey data, which do not only underestimate the actual prevalence but are also only available on coarse spatial scales. The discussion of including area deprivation for planning of healthcare is still relatively young in Germany, although previous studies have shown that area deprivation is associated with adverse health outcomes, irrespective of individual characteristics. The aim of this study is therefore to analyze the spatial distribution of hypertension at very fine geographic scales and to assess location-specific associations between hypertension, socio-demographic population characteristics and area deprivation based on health insurance claims of the AOK Nordost.MethodsTo visualize the spatial distribution of hypertension prevalence at very fine geographic scales, we used the conditional autoregressive Besag–York–Mollié (BYM) model. Geographically weighted regression modelling (GWR) was applied to analyze the location-specific association of hypertension to area deprivation and further socio-demographic population characteristics.ResultsThe sex- and age-adjusted prevalence of hypertension was 33.1% in 2012 and varied widely across northeastern Germany. The main risk factors for hypertension were proportions of insurants aged 45–64, 65 and older, area deprivation and proportion of persons commuting to work outside their residential municipality. The GWR model revealed important regional variations in the strength of the examined associations.ConclusionArea deprivation has only a significant and therefore direct influence in large parts of Mecklenburg-West Pomerania. However, the spatially varying strength of the association between demographic variables and hypertension indicates that there also exists an indirect effect of area deprivation on the prevalence of hypertension. It can therefore be expected that persons ageing in deprived areas will be at greater risk of hypertension, irrespective of their individual characteristics. The future planning and allocation of primary healthcare in northeastern Germany would therefore greatly benefit from considering the effect of area deprivation.

Lightning Prediction for Australia Using Multivariate Analyses of Large-Scale Atmospheric Variables

AbstractLightning is a natural hazard that can lead to the ignition of wildfires, disruption and damage to power and telecommunication infrastructures, human and livestock injuries and fatalities, and disruption to airport activities. This paper examines the ability of six statistical and machine-learning classification techniques to distinguish between nonlightning and lightning days at the coarse spatial and temporal scales of current general circulation models and reanalyses. The classification techniques considered were 1) a combination of principal component analysis and logistic regression, 2) classification and regression trees, 3) random forests, 4) linear discriminant analysis, 5) quadratic discriminant analysis, and 6) logistic regression. Lightning-flash counts at six locations across Australia for 2004–13 were used, together with atmospheric variables from the ERA-Interim dataset. Tenfold cross validation was used to evaluate classification performance. It was found that logistic regression was superior to the other classifiers considered and that its prediction skill is much better than using climatological values. The sets of atmospheric variables included in the final logistic-regression models were primarily composed of spatial mean measures of instability and lifting potential, along with atmospheric water content. The memberships of these sets varied among climatic zones.

Ross River Virus and the Necessity of Multiscale, Eco-epidemiological Analyses.

Zoonotic vector-borne disease prevalence is affected by vector, human, and reservoir host factors, which are influenced by habitat and climate; these 5 components interact on microhabitat-to-landscape scales but are often analyzed at a single spatial scale. We present an information theoretic, multiscale, multiple regression analysis of the ecological drivers of Ross River virus. We analyze the spatial pattern of 20 years of Ross River virus infections from South Australia (1992-2012; n = 5261), using variables across these 5 components of disease ecology at 3 spatial scales. We found that covariate importance depended on the spatial scale of the analysis; some biotic variables were more important at fine scales and some abiotic variables were more important at coarser spatial scales. The urban score of an area was most predictive of infections, and mosquito variables did not improve the explanatory power of these models. Through this multiscale analysis, we identified novel drivers of the spatial distribution of disease and recommend public health interventions. Our results underline that single-scale analyses may paint an incomplete picture of disease drivers, potentially creating a major flaw in epidemiological analyses. Multiscale, ecological analyses are needed to better understand infectious disease transmission.

Macroecological factors shape local-scale spatial patterns in agriculturalist settlements.

Macro-scale patterns of human systems ranging from population distribution to linguistic diversity have attracted recent attention, giving rise to the suggestion that macroecological rules shape the assembly of human societies. However, in which aspects the geography of our own species is shaped by macroecological factors remains poorly understood. Here, we provide a first demonstration that macroecological factors shape strong local-scale spatial patterns in human settlement systems, through an analysis of spatial patterns in agriculturalist settlements in eastern mainland China based on high-resolution Google Earth images. We used spatial point pattern analysis to show that settlement spatial patterns are characterized by over-dispersion at fine spatial scales (0.05-1.4 km), consistent with territory segregation, and clumping at coarser spatial scales beyond the over-dispersion signals, indicating territorial clustering. Statistical modelling shows that, at macroscales, potential evapotranspiration and topographic heterogeneity have negative effects on territory size, but positive effects on territorial clustering. These relationships are in line with predictions from territory theory for hunter-gatherers as well as for many animal species. Our results help to disentangle the complex interactions between intrinsic spatial processes in agriculturalist societies and external forcing by macroecological factors. While one may speculate that humans can escape ecological constraints because of unique abilities for environmental modification and globalized resource transportation, our work highlights that universal macroecological principles still shape the geography of current human agricultural societies.

Landscape heterogeneity as a surrogate of biodiversity in mountain systems: What is the most appropriate spatial analytical unit?

The estimated potential of landscape metrics as a surrogate for biodiversity is strongly dependent on the spatial analytical unit used for evaluation. We assessed the relationship between terrestrial vertebrate species richness (total and taxonomic) and structural landscape heterogeneity, testing the impact of using different spatial analytical units in three mountain systems in Spain. Landscape heterogeneity was quantified through an additive partitioning of the Shannon diversity index of landscape classes. Both landscape heterogeneity and species richness were calculated using two spatial analytical unit approaches: eco-geographic vs. arbitrary (i.e., watersheds vs. square windows of different sizes 20×20km, 50×50km, 100×100km). We predicted species richness on the basis of landscape heterogeneity by fitting separate linear models for each spatial analytical unit approach. The main results obtained showed that landscape heterogeneity influenced terrestrial vertebrate species richness. However, the emerging relationships were dependent on the spatial analytical unit approach. The eco-geographic approach showed significant relationships between landscape heterogeneity and total and taxonomic species richness in almost all cases (except mammals). Considering the arbitrary approach, landscape heterogeneity appeared as a predictor of species richness only for mammals and breeding birds and at the coarsest spatial scales. Our results claim for further consideration of eco-geographical spatial analytical unit approaches in biodiversity studies and show that the methods of this study offer a valuable cost-effective framework for biodiversity management and spatial modeling, with potential to be adapted to national and global applications.

Indices of abundance in the Gulf of Mexico reef fish complex: A comparative approach using spatial data from vessel monitoring systems

The Gulf of Mexico reef fish complex is socioeconomically important and is exploited by a vertical line fishery capable of high resolution spatial targeting. Indices of abundance derived from fishery dependent catch-per-unit-effort (CPUE) data are an important input to the assessment of these stocks. Traditionally, these indices have been derived from standardized logbook data, aggregated at a coarse spatial scale, and are limited to generating predictions for observed spatiotemporal strata. Understanding how CPUE is spatially distributed, however, can help identify range contractions and avoid hyperstability or hyperdepletion, both of which can mask the true population dynamics. Vessel monitoring systems (VMS) can provide complete, high-resolution distributions of CPUE used to create abundance indices. Here we compare two methods — spatial averaging of VMS-derived catch and effort data and the result of generalized linear models applied to logbook data for generating indices, to evaluate the use of VMS-derived abundance indices in assessments of reef fish stocks. This work suggests that in fisheries where targeting occurs at very fine spatial scales, abundance indices derived from high-resolution, spatiotemporally complete data may more accurately reflect the underlying dynamics of the stock.

Reconstructing Ecological Niche Evolution When Niches Are Incompletely Characterized

Evolutionary dynamics of abiotic ecological niches across phylogenetic history can shed light on large-scale biogeographic patterns, macroevolutionary rate shifts, and the relative ability of lineages to respond to global change. An unresolved question is how best to represent and reconstruct evolution of these complex traits at coarse spatial scales through time. Studies have approached this question by integrating phylogenetic comparative methods with niche estimates inferred from correlative and other models. However, methods for estimating niches often produce incomplete characterizations, as they are inferred from present-day distributions that may be limited in full expression of the fundamental ecological niche by biotic interactions, dispersal limitations, and the existing set of environmental conditions. Here, we test whether incomplete niche characterizations inherent in most estimates of species' niches bias phylogenetic reconstructions of niche evolution, using simulations of virtual species with known niches. Results establish that incompletely characterized niches inflate estimates of evolutionary change and lead to error in ancestral state reconstructions. Our analyses also provide a potential mechanism to explain the frequent observation that maximum thermal tolerances are more conserved than minimum thermal tolerances: populations and species experience more spatial variation in minimum temperature than in maximum temperature across their distributions and, consequently, may experience stronger diversifying selection for cold tolerance.

Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020

BackgroundThere are growing demands for predicting the prospects of achieving the global elimination of neglected tropical diseases as a result of the institution of large-scale nation-wide intervention programs by the WHO-set target year of 2020. Such predictions will be uncertain due to the impacts that spatial heterogeneity and scaling effects will have on parasite transmission processes, which will introduce significant aggregation errors into any attempt aiming to predict the outcomes of interventions at the broader spatial levels relevant to policy making. We describe a modeling platform that addresses this problem of upscaling from local settings to facilitate predictions at regional levels by the discovery and use of locality-specific transmission models, and we illustrate the utility of using this approach to evaluate the prospects for eliminating the vector-borne disease, lymphatic filariasis (LF), in sub-Saharan Africa by the WHO target year of 2020 using currently applied or newly proposed intervention strategies.Methods and ResultsWe show how a computational platform that couples site-specific data discovery with model fitting and calibration can allow both learning of local LF transmission models and simulations of the impact of interventions that take a fuller account of the fine-scale heterogeneous transmission of this parasitic disease within endemic countries. We highlight how such a spatially hierarchical modeling tool that incorporates actual data regarding the roll-out of national drug treatment programs and spatial variability in infection patterns into the modeling process can produce more realistic predictions of timelines to LF elimination at coarse spatial scales, ranging from district to country to continental levels. Our results show that when locally applicable extinction thresholds are used, only three countries are likely to meet the goal of LF elimination by 2020 using currently applied mass drug treatments, and that switching to more intensive drug regimens, increasing the frequency of treatments, or switching to new triple drug regimens will be required if LF elimination is to be accelerated in Africa. The proportion of countries that would meet the goal of eliminating LF by 2020 may, however, reach up to 24/36 if the WHO 1% microfilaremia prevalence threshold is used and sequential mass drug deliveries are applied in countries.ConclusionsWe have developed and applied a data-driven spatially hierarchical computational platform that uses the discovery of locally applicable transmission models in order to predict the prospects for eliminating the macroparasitic disease, LF, at the coarser country level in sub-Saharan Africa. We show that fine-scale spatial heterogeneity in local parasite transmission and extinction dynamics, as well as the exact nature of intervention roll-outs in countries, will impact the timelines to achieving national LF elimination on this continent.

Scale dependence of environmental controls on the functional diversity of coral reef fish communities

AbstractAimThe functional composition of local assemblages is hypothesized to be controlled by hierarchical environmental filters, whereby the importance of different abiotic and biotic factors varies across both spatial scales and the different dimensions of functional diversity. We examine scale dependence in functional diversity–environment relationships with the ultimate aim of advancing models that predict the response of functional diversity to global change.LocationCoral reefs surrounding 23 minimally disturbed central‐western Pacific islands.Time period2010–2015.Major taxa studiedCoral reef fishes.MethodsWe surveyed 1,423 reef sites using a standardized monitoring protocol and classified the 547 taxa encountered based on traits related to resource use, body size and behaviour. For each fish community, we calculated species richness and three metrics of functional diversity: functional richness, functional redundancy and functional evenness. We then built nested models at three spatial scales to evaluate the predictive power of environmental conditions over each component of functional diversity.ResultsClimatic variables (e.g., primary productivity) and geomorphic context (e.g., bathymetric slope) were more important in predicting functional diversity at coarse spatial scales. In contrast, local measures of habitat quality, including benthic complexity, depth and hard coral cover, were generally most important at finer scales. All diversity metrics were better predicted at coarser scales, but which predictors were important varied among metrics.Main conclusionsThe observed scale dependence in environmental predictors of functional diversity generally matches models of hierarchical filters on functional community assembly. Contrary to expectation, however, functional evenness and functional redundancy, which incorporate information on biomass distributions, were not better predicted at finer spatial scales. Instead, broad‐scale variation in environmental variables was most important in predicting all components of functional diversity. Furthermore, the distinct responses of each functional diversity metric to environmental variation indicate that each measures a unique dimension of reef‐fish diversity, and environmental change may affect each differently.

Identifying (subsurface) anthropogenic heat sources that influence temperature in the drinking water distribution system

Abstract. The water temperature in the drinking water distribution system and at customers' taps approaches the surrounding soil temperature at a depth of 1 m. Water temperature is an important determinant of water quality. In the Netherlands drinking water is distributed without additional residual disinfectant and the temperature of drinking water at customers' taps is not allowed to exceed 25 °C. In recent decades, the urban (sub)surface has been getting more occupied by various types of infrastructures, and some of these can be heat sources. Only recently have the anthropogenic sources and their influence on the underground been studied on coarse spatial scales. Little is known about the urban shallow underground heat profile on small spatial scales, of the order of 10 m × 10 m. Routine water quality samples at the tap in urban areas have shown up locations – so-called hotspots – in the city, with relatively high soil temperatures – up to 7 °C warmer – compared to the soil temperatures in the surrounding rural areas. Yet the sources and the locations of these hotspots have not been identified. It is expected that with climate change during a warm summer the soil temperature in the hotspots can be above 25 °C. The objective of this paper is to find a method to identify heat sources and urban characteristics that locally influence the soil temperature. The proposed method combines mapping of urban anthropogenic heat sources, retrospective modelling of the soil temperature, analysis of water temperature measurements at the tap, and extensive soil temperature measurements. This approach provided insight into the typical range of the variation of the urban soil temperature, and it is a first step to identifying areas with potential underground heat stress towards thermal underground management in cities.