The Importance of Spatial Scale in Habitat Models: Capercaillie in the Swiss Alps

The role of scale in ecology is widely recognized as being of vital importance for understanding ecological patterns and processes. The capercaillie (Tetrao urogallus) is a forest grouse species with large spatial requirements and highly specialized habitat preferences. Habitat models at the forest stand scale can only partly explain capercaillie occurrence, and some studies at the landscape scale have emphasized the role of large-scale effects. We hypothesized that both the ability of single variables and multivariate models to explain capercaillie occurrence would vary with the spatial scale of the analysis. To test this hypothesis, we varied the grain size of our analysis from 1 to just over 1100hectares and built univariate and multivariate habitat suitability models for capercaillie in the Swiss Alps. The variance explained by the univariate models was found to vary among the predictors and with spatial scale. Within the multivariate models, the best single-scale model (using all predictor variables at the same scale) worked at a scale equivalent to a small annual home range. The multi-scale model, in which each predictor variable was entered at the scale at which it had performed best in the univariate model, did slightly better than the best single-scale model. Our results confirm that habitat variables should be included at different spatial scales when species-habitat relationships are investigate

Diatom beta-diversity in streams increases with spatial scale and decreases with nutrient enrichment across regional to sub-continental scales

Aim To quantify the relative contributions of local community assembly processes versus gamma-diversity to beta-diversity, and to assess how spatial scale and anthropogenic disturbance (i.e. nutrient enrichment) interact to dictate which driver dominates. Location France and the United States. Time period 1993-2011. Major taxa studied Freshwater stream diatoms. Methods beta-diversity along a nutrient enrichment gradient was examined across multiple spatial scales. beta-diversity was estimated using multi-site Sorensen dissimilarity. We assessed the relative importance of specialists versus generalists using Friedley coefficient, and the contribution of local community assembly versus gamma-diversity to beta-diversity across spatial scales, with a null model. Finally, we estimated the response of beta-diversity to environmental and spatial factors by testing the correlations between community, environmental and geographical distance matrices with partial Mantel tests. Results beta-diversity generally increased with spatial scale but the rate of increase depended on nutrient enrichment level. beta-diversity decreased significantly with increasing nutrient enrichment level due to the loss of specialist species. Local assembly was an important driver of beta-diversity especially under low nutrient enrichment. Significant partial Mantel correlations were observed between diatom beta-diversity and pure environmental distances under these conditions, highlighting the role of species sorting in local assembly processes. Conversely, in heavily enriched sites, only spatial distances were significantly correlated with beta-diversity, which indicated a substantial role of dispersal processes. Main conclusions Nutrient concentration mediated the expected increase in beta-diversity with spatial scales. Across spatial scales, beta-diversity was more influenced by local assembly processes rather than by gamma-diversity. Nutrient enrichment was associated with an overall decline in diatom beta-diversity and a shift in assembly processes from species sorting to dispersal, notably due to the elimination of some specialists and their subsequent replacement by generalists.Peer reviewe

Statistical wave climate projections for coastal impact assessments

Global multimodel wave climate projections are obtained at 1.0° × 1.0° scale from 30 Coupled Model Intercomparison Project Phase 5 (CMIP5) global circulation model (GCM) realizations. A semi-supervised weather-typing approach based on a characterization of the ocean wave generation areas and the historical wave information from the recent GOW2 database are used to train the statistical model. This framework is also applied to obtain high resolution projections of coastal wave climate and coastal impacts as port operability and coastal flooding. Regional projections are estimated using the collection of weather types at spacing of 1.0°. This assumption is feasible because the predictor is defined based on the wave generation area and the classification is guided by the local wave climate. The assessment of future changes in coastal impacts is based on direct downscaling of indicators defined by empirical formulations (total water level for coastal flooding and number of hours per year with overtopping for port operability). Global multimodel projections of the significant wave height and peak period are consistent with changes obtained in previous studies. Statistical confidence of expected changes is obtained due to the large number of GCMs to construct the ensemble. The proposed methodology is proved to be flexible to project wave climate at different spatial scales. Regional changes of additional variables as wave direction or other statistics can be estimated from the future empirical distribution with extreme values restricted to high percentiles (i.e., 95th, 99th percentiles). The statistical framework can also be applied to evaluate regional coastal impacts integrating changes in storminess and sea level rise.The authors acknowledge the support of the Spanish Ministerio de Economía y Competitividad (MINECO) and European Regional Development Fund (FEDER) under Grant BIA2015-70644-R (MINECO/FEDER, UE). The authors are grateful to Nicolás Ripoll for his help in the performing the statistical simulations. The DAC data is produced by CLS Space Oceanography Division and distributed by Aviso, with support from Cnes (http://www.aviso.altimetry.fr/). The CMIP5 sea level pressure data are available at http://cmip-pcmdi.llnl.gov/cmip5/data_portal.html. Mean sea level projections are available at ftp://ftp.icdc.zmaw.de/ar5_sea_level_rise

Racial differences in the built environment—body mass index relationship? A geospatial analysis of adolescents in urban neighborhoods

Background: Built environment features of neighborhoods may be related to obesity among adolescents and potentially related to obesity-related health disparities. The purpose of this study was to investigate spatial relationships between various built environment features and body mass index (BMI) z-score among adolescents, and to investigate if race/ethnicity modifies these relationships. A secondary objective was to evaluate the sensitivity of findings to the spatial scale of analysis (i.e. 400- and 800-meter street network buffers). Methods: Data come from the 2008 Boston Youth Survey, a school-based sample of public high school students in Boston, MA. Analyses include data collected from students who had georeferenced residential information and complete and valid data to compute BMI z-score (n = 1,034). We built a spatial database using GIS with various features related to access to walking destinations and to community design. Spatial autocorrelation in key study variables was calculated with the Global Moran’s I statistic. We fit conventional ordinary least squares (OLS) regression and spatial simultaneous autoregressive error models that control for the spatial autocorrelation in the data as appropriate. Models were conducted using the total sample of adolescents as well as including an interaction term for race/ethnicity, adjusting for several potential individual- and neighborhood-level confounders and clustering of students within schools. Results: We found significant positive spatial autocorrelation in the built environment features examined (Global Moran’s I most ≥ 0.60; all p = 0.001) but not in BMI z-score (Global Moran’s I = 0.07, p = 0.28). Because we found significant spatial autocorrelation in our OLS regression residuals, we fit spatial autoregressive models. Most built environment features were not associated with BMI z-score. Density of bus stops was associated with a higher BMI z-score among Whites (Coefficient: 0.029, p < 0.05). The interaction term for Asians in the association between retail destinations and BMI z-score was statistically significant and indicated an inverse association. Sidewalk completeness was significantly associated with a higher BMI z-score for the total sample (Coefficient: 0.010, p < 0.05). These significant associations were found for the 800-meter buffer. Conclusion: Some relationships between the built environment and adolescent BMI z-score were in the unexpected direction. Our findings overall suggest that the built environment does not explain a large proportion of the variation in adolescent BMI z-score or racial disparities in adolescent obesity. However, there are some differences by race/ethnicity that require further research among adolescents

A Self-adaptive Discriminative Autoencoder for Medical Applications

Computer aided diagnosis (CAD) systems play an essential role in the early detection and diagnosis of developing disease for medical applications. In order to obtain the highly recognizable representation for the medical images, a self-adaptive discriminative autoencoder (SADAE) is proposed in this paper. The proposed SADAE system is implemented under a deep metric learning framework which consists of K local autoencoders, employed to learn the K subspaces that represent the diverse distribution of the underlying data, and a global autoencoder to restrict the spatial scale of the learned representation of images. Such community of autoencoders is aided by a self-adaptive metric learning method that extracts the discriminative features to recognize the different categories in the given images. The quality of the extracted features by SADAE is compared against that of those extracted by other state-of-the-art deep learning and metric learning methods on five popular medical image data sets. The experimental results demonstrate that the medical image recognition results gained by SADAE are much improved over those by the alternatives

Mapping poverty at multiple geographical scales

Poverty mapping is a powerful tool to study the geography of poverty. The choice of the spatial resolution is central as poverty measures defined at a coarser level may mask their heterogeneity at finer levels. We introduce a small area multi-scale approach integrating survey and remote sensing data that leverages information at different spatial resolutions and accounts for hierarchical dependencies, preserving estimates coherence. We map poverty rates by proposing a Bayesian Beta-based model equipped with a new benchmarking algorithm that accounts for the double-bounded support. A simulation study shows the effectiveness of our proposal and an application on Bangladesh is discussed.Comment: 22 pages, 7 figure

A classification algorithm for selective dynamical downscaling of precipitation extremes

High-resolution climate data O(1km) at the catchment scale can be of great value to both hydrological modellers and end users, in particular for the study of extreme precipitation. While dynamical downscaling with convection-permitting models is a valuable approach for producing quality high-resolution O(1km) data, its added value can often not be realized due to the prohibitive computational expense. Here we present a novel and flexible classification algorithm for discriminating between days with an elevated potential for extreme precipitation over a catchment and days without, so that dynamical downscaling to convection-permitting resolution can be selectively performed on high-risk days only, drastically reducing total computational expense compared to continuous simulations; the classification method can be applied to climate model data or reanalyses. Using observed precipitation and the corresponding synoptic-scale circulation patterns from reanalysis, characteristic extremal circulation patterns are identified for the catchment via a clustering algorithm. These extremal patterns serve as references against which days can be classified as potentially extreme, subject to additional tests of relevant meteorological predictors in the vicinity of the catchment. Applying the classification algorithm to reanalysis, the set of potential extreme days (PEDs) contains well below 10% of all days, though it includes essentially all extreme days; applying the algorithm to reanalysis-driven regional climate simulations over Europe (12km resolution) shows similar performance, and the subsequently dynamically downscaled simulations (2km resolution) well reproduce the observed precipitation statistics of the PEDs from the training period. Additional tests on continuous 12km resolution historical and future (RCP8.5) climate simulations, downscaled in 2km resolution time slices, show the algorithm again reducing the number of days to simulate by over 90% and performing consistently across climate regimes. The downscaling framework we propose represents a computationally inexpensive means of producing high-resolution climate data, focused on extreme precipitation, at the catchment scale, while still retaining the advantages of convection-permitting dynamical downscaling

Zoning and weighting in urban heat island vulnerability and risk mapping in Helsinki, Finland

Climate change is likely to increase the risks related to heat waves in urban areas. We map spatial pattern of heat wave vulnerability and risk in the Helsinki metropolitan area in southern Finland. First, we assess differences that zoning, i.e., differences in spatial units of analysis, and weighting, i.e., weights given to indicators when constructing the index, cause in map production. Second, we evaluate how maps of consensus and certainty could pave the way for visualizing and assessing uncertainties in risk and vulnerability indices. For vulnerability, we use socioeconomic data using 5 different zoning options and 11 different weighting options. For risk, we add two extra layers to vulnerability maps: hazard map showing the spatial pattern of heat based on Landsat satellite images and exposure map showing the spatial pattern of population. We found that when different zoning options are used, the spatial pattern of vulnerability may differ dramatically. In risk maps, the differences between zoning options are smaller. Contrary to previous literature, differences in indicator weighting alter the final maps slightly. The consensus and certainty maps show their potential, e.g., in pointing out areas which may have both high risk/vulnerability and high certainty for risk/vulnerability. Finally, we discuss other possibilities in tackling the uncertainties in mapping and propose new avenues for research.Peer reviewe

Air temperature forecasting using machine learning techniques: a review

Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined