scholarly journals Spatial analysis of the distribution of intestinal nematode infections in Uganda

2004 ◽  
Vol 132 (6) ◽  
pp. 1065-1071 ◽  
Author(s):  
S. BROOKER ◽  
N. B. KABATEREINE ◽  
E. M. TUKAHEBWA ◽  
F. KAZIBWE
Keyword(s):  
Spatial Structure ◽  
Land Surface ◽  
Large Scale ◽  
Spatial Epidemiology ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Spatial Dependency ◽  
Geostatistical Methods ◽  
Semivariogram Analysis ◽  
Intestinal Nematode

The spatial epidemiology of intestinal nematodes in Uganda was investigated using generalized additive models and geostatistical methods. The prevalence of Ascaris lumbricoides and Trichuris trichiura was unevenly distributed in the country with prevalence greatest in southwest Uganda whereas hookworm was more homogeneously distributed. A. lumbricoides and T. Trichiura prevalence were nonlinearly related to satellite sensor-based estimates of land surface temperature; hookworm was nonlinearly associated with rainfall. Semivariogram analysis indicated that T. trichiura prevalence exhibited no spatial structure and that A. lumbricoides exhibited some spatial dependency at small spatial distances, once large-scale, mainly environmental, trends had been removed. In contrast, there was much more spatial structure in hookworm prevalence although the underlying factors are at present unclear. The implications of the results are discussed in relation to parasite spatial epidemiology and the prediction of infection distributions.

scholarly journals Present and LGM permafrost from climate simulations: contribution of statistical downscaling

2010 ◽  
Vol 4 (4) ◽  
pp. 2233-2275 ◽  
Author(s):  
G. Levavasseur ◽  
M. Vrac ◽  
D. M. Roche ◽  
D. Paillard ◽  
A. Martin ◽  
...  
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Multinomial Logistic Regression ◽  
Generalized Additive Models ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Additive Models ◽  
Simple Relationship ◽  
Time Period

Abstract. We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the inter-variability between them. Studying an heterogeneous variable such as permafrost implies to conduct analysis at a smaller spatial scale compared with climate models resolution. Our approach consists in applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of surface air temperature (SAT). Then, we define permafrost distribution over Eurasia by SAT conditions. In a first validation step on present climate (CTRL period), GAM shows some limitations with non-systemic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a stochastic generator approach through a Multinomial Logistic Regression (MLR), which directly models the probabilities of local permafrost indices. The obtained permafrost distributions appear in a better agreement with data. In both cases, the provided local information reduces the inter-variability between climate models. Nevertheless, this also proves that a simple relationship between permafrost and the SAT only is not always sufficient to represent local permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. Our SDMs do not significantly improve permafrost distribution and do not reduce the inter-variability between climate models, at this period. We show that LGM permafrost distribution from climate models strongly depends on large-scale SAT. The differences with LGM data, larger than in the CTRL period, reduce the contribution of downscaling and depend on several factors deserving further studies.

scholarly journals Linking trophic cascades to changes in desert dune geomorphology using high-resolution drone data

2018 ◽  
Vol 15 (144) ◽  
pp. 20180327 ◽  
Author(s):  
Mitchell B. Lyons ◽  
Charlotte H. Mills ◽  
Christopher E. Gordon ◽  
Mike Letnic
Keyword(s):  
Vegetation Cover ◽  
Large Scale ◽  
Generalized Additive Models ◽  
Three Dimensional ◽  
Trophic Cascades ◽  
Stable State ◽  
Physical Characteristics ◽  
Additive Models ◽  
Surface Erosion ◽  
Dune Stability

Vegetation cover is fundamental in the formation and maintenance of geomorphological features in dune systems. In arid Australia, increased woody shrub cover has been linked to removal of the apex predator (Dingoes, Canis dingo ) via subsequent trophic cascades. We ask whether this increase in shrubs can be linked to altered physical characteristics of the dunes. We used drone-based remote sensing to measure shrub density and construct three-dimensional models of dune morphology. Dunes had significantly different physical characteristics either side of the ‘dingo-proof fence’, inside which dingoes are systematically eradicated and shrub density is higher over vast spatial extents. Generalized additive models revealed that dunes with increased shrub density were higher, differently shaped and more variable in height profile. We propose that low shrub density induces aeolian and sedimentary processes that result in greater surface erosion and sediment transport, whereas high shrub density promotes dune stability. We speculate that increased vegetation cover acts to push dunes towards an alternate stable state, where climatic variation no longer has a significant effect on their morphodynamic state within the bi-stable state model. Our study provides evidence that anthropogenically induced trophic cascades can indirectly lead to large-scale changes in landscape geomorphology.

Investigating spatial variation and temperature effects on maturity of female winter flounder (Pseudopleuronectes americanus) using generalized additive models

2014 ◽  
Vol 71 (9) ◽  
pp. 1279-1290 ◽  
Author(s):  
Megan V. Winton ◽  
Mark J. Wuenschel ◽  
Richard S. McBride
Keyword(s):  
Spatial Structure ◽  
Spatial Variation ◽  
Clustering Algorithm ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Winter Flounder ◽  
Effect Of Temperature ◽  
Spatial Effects ◽  
Age At Maturity ◽  
Pseudopleuronectes Americanus

Generalized additive models were used to investigate fine-scale spatial variation in female maturity across the three United States’ winter flounder (Pseudopleuronectes americanus) stocks. The effect of temperature on maturity was also investigated. Maturity models explicitly incorporating spatial structure performed better than “traditional” methods incorporating spatial effects by aggregating data according to predefined stock boundaries. Models including temperature explained more of the variability in maturity than those based only on fish size or age but did not improve fit over models incorporating spatial structure. Based on the size- and age-at-maturity estimates from the spatially explicit models, distinct subareas were objectively identified using a spatially constrained clustering algorithm. The results suggested greater variation in size- and age-at-maturity within than between existing stock areas. The approach outlined here provides a method for identifying areas with different vital rates without the need to presume subjective boundaries.

Local Regimes of Atmospheric Variability: A Case Study of Southern California

2006 ◽  
Vol 19 (17) ◽  
pp. 4308-4325 ◽  
Author(s):  
Sebastien Conil ◽  
Alex Hall
Keyword(s):  
Spatial Structure ◽  
Land Surface ◽  
General Circulation ◽  
Large Scale ◽  
Spatial Scales ◽  
Surface Wind ◽  
General Circulation Models ◽  
Climate Impacts ◽  
Atmospheric Variability ◽  
Composite Surface

Abstract The primary regimes of local atmospheric variability are examined in a 6-km regional atmospheric model of the southern third of California, an area of significant land surface heterogeneity, intense topography, and climate diversity. The model was forced by reanalysis boundary conditions over the period 1995–2003. The region is approximately the same size as a typical grid box of the current generation of general circulation models used for global climate prediction and reanalysis product generation, and so can be thought of as a laboratory for the study of climate at spatial scales smaller than those resolved by global simulations and reanalysis products. It is found that the simulated circulation during the October–March wet season, when variability is most significant, can be understood through an objective classification technique in terms of three wind regimes. The composite surface wind patterns associated with these regimes exhibit significant spatial structure within the model domain, consistent with the complex topography of the region. These regimes also correspond nearly perfectly with the simulation’s highly structured patterns of variability in hydrology and temperature, and therefore are the main contributors to the local climate variability. The regimes are approximately equally likely to occur regardless of the phase of the classical large-scale modes of atmospheric variability prevailing in the Pacific–North American sector. The high degree of spatial structure of the local regimes and their tightly associated climate impacts, as well as their ambiguous relationship with the primary modes of large-scale variability, demonstrate that the local perspective offered by the high-resolution model is necessary to understand and predict the climate variations of the region.

Advances in Understanding Landscape Influences on Freshwater Habitats and Biological Assemblages

2019 ◽  
Keyword(s):  
Large Scale ◽  
River Flow ◽  
Fish Assemblages ◽  
Generalized Additive Models ◽  
Sampling Site ◽  
Monitoring Program ◽  
Additive Models ◽  
Species Lists ◽  
Freshwater Habitats ◽  
Eastern Mosquitofish

<i>Abstract.</i>—We document a simple electrofishing-only monitoring program for assessing fish assemblages across large spatial extents. First, we demonstrate the justification for using only electrofishing for the monitoring. Second, we demonstrate the usefulness of having a well-designed surveillance-monitoring program in place to demonstrate the effect of landscape disturbances. Implementing electrofishing alone means that multiple sites can be sampled in a single day and there is no need to return to clear nets or traps within a sampling site. Whereas electrofishing alone does not return full species lists within sampled sites, we demonstrate that when data are aggregated up to the watershed or catchment extent, more than 90% of species are included. Analyses that do not require a census of species, such as bioassessment of river health can be readily carried out using electrofishing data. The Murray–Darling basin, Australia, was sampled with the recommended large-extent electrofishing program between 2004 and 2012, a period that saw the region subjected to large-scale variations in river flow levels spatially and temporally. We fit generalized additive models to the electrofishing data in conjunction with river flow data to document large-extent relationships between fish species occurrence and relative flow levels for the previous 3 d, 3 months, or 3 years. We found that several small-bodied species, Eastern Mosquitofish <i>Gambusia holbrooki</i>, Flathead Gudgeon <i>Philypnodon grandiceps</i>, and Australian Smelt <i>Retropinna semoni</i>, were more likely to be collected when conditions were drier in the past 3 d to 3 months, whereas common medium and large-bodied species were less likely to be collected when flow was lower over the previous 3 months to 3 years.

Computational Issues for Large-Scale Land Surface Data Assimilation Problems

2006 ◽  
Vol 7 (3) ◽  
pp. 494-510 ◽  
Author(s):  
Dennis McLaughlin ◽  
Yuhua Zhou ◽  
Dara Entekhabi ◽  
Virat Chatdarong
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Spatial Structure ◽  
Computational Efficiency ◽  
Land Surface ◽  
Kalman Filters ◽  
Large Scale ◽  
Singular Value ◽  
High Dimensionality ◽  
Surface Data

Abstract Land surface data assimilation problems are often limited by the high dimensionality of states created by spatial discretization over large high-resolution computational grids. Yet field observations and simulation both confirm that soil moisture can have pronounced spatial structure, especially after extensive rainfall. This suggests that the high dimensionality of the problem could be reduced during wet periods if spatial patterns could be more efficiently represented. After prolonged drydown, when spatial structure is determined primarily by small-scale soil and vegetation variability rather than rainfall, the original high-dimensional problem can be effectively replaced by many independent low-dimensional problems that can be solved in parallel with relatively little effort. In reality, conditions are continually varying between these two extremes. This is confirmed by a singular value decomposition of the replicate matrix (covariance square root) produced in an ensemble forecasting simulation experiment. The singular value spectrum drops off quickly after rainfall events, when a few leading modes dominate the spatial structure of soil moisture. The spectrum is much flatter after a prolonged drydown period, when spatial structure is less significant. Deterministic reduced-rank Kalman filters can achieve significant computational efficiency by focusing on the leading modes of a system with large-scale spatial structure. But these methods are not well suited for land surface problems with complex uncertain inputs and rapidly changing spectra. Local ensemble Kalman filters are suitable for such problems during dry periods but give less accurate results after rainfall. The most promising option for achieving computational efficiency and accuracy is to develop generalized localization methods that dynamically aggregate states, reflecting structural changes in the ensemble.

scholarly journals Leukocyte Profiles Reflect Geographic Range Limits in a Widespread Neotropical Bat

2019 ◽  
Vol 59 (5) ◽  
pp. 1176-1189 ◽  
Author(s):  
Daniel J Becker ◽  
Cecilia Nachtmann ◽  
Hernan D Argibay ◽  
Germán Botto ◽  
Marina Escalera-Zamudio ◽  
...  
Keyword(s):  
Large Scale ◽  
Generalized Additive Models ◽  
Local Environment ◽  
Field Studies ◽  
Geographic Range ◽  
Immune Defense ◽  
Additive Models ◽  
Wild Populations ◽  
Predator Abundance ◽  
Leukocyte Profiles

Abstract Quantifying how the environment shapes host immune defense is important for understanding which wild populations may be more susceptible or resistant to pathogens. Spatial variation in parasite risk, food and predator abundance, and abiotic conditions can each affect immunity, and these factors can also manifest at both local and biogeographic scales. Yet identifying predictors and the spatial scale of their effects is limited by the rarity of studies that measure immunity across many populations of broadly distributed species. We analyzed leukocyte profiles from 39 wild populations of the common vampire bat (Desmodus rotundus) across its wide geographic range throughout the Neotropics. White blood cell differentials varied spatially, with proportions of neutrophils and lymphocytes varying up to six-fold across sites. Leukocyte profiles were spatially autocorrelated at small and very large distances, suggesting that local environment and large-scale biogeographic factors influence cellular immunity. Generalized additive models showed that bat populations closer to the northern and southern limits of the species range had more neutrophils, monocytes, and basophils, but fewer lymphocytes and eosinophils, than bats sampled at the core of their distribution. Habitats with access to more livestock also showed similar patterns in leukocyte profiles, but large-scale patterns were partly confounded by time between capture and sampling across sites. Our findings suggest that populations at the edge of their range experience physiologically limiting conditions that predict higher chronic stress and greater investment in cellular innate immunity. High food abundance in livestock-dense habitats may exacerbate such conditions by increasing bat density or diet homogenization, although future spatially and temporally coordinated field studies with common protocols are needed to limit sampling artifacts. Systematically assessing immune function and response over space will elucidate how environmental conditions influence traits relevant to epidemiology and help predict disease risks with anthropogenic disturbance, land conversion, and climate change.

scholarly journals Partitioning resources through the seasons: abundance and phenology of carrion beetles (Silphidae) in southeastern Ontario

2021 ◽  
Author(s):  
Jillian Wettlaufer ◽  
Kevin William Burke ◽  
David Vincent Beresford ◽  
Paul Martin
Keyword(s):  
Resource Partitioning ◽  
Large Scale ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Seasonal Abundance ◽  
Similar Species ◽  
Beetle Species ◽  
Seasonal Differences ◽  
Carrion Beetle ◽  
Carrion Beetles

The coexistence of ecologically similar species is thought to require resource partitioning to minimize competition. Phenological, seasonal differences in activity may provide an important axis for resource partitioning. Here, we test for evidence of seasonal differences in activity within a diverse guild of carrion beetles (Silphidae) in a habitat preserve on the Frontenac Arch, southeastern Ontario, Canada using a large-scale survey during their active seasons (April to October). We then used generalized additive models to test for differences in seasonal abundance among eight co-occurring carrion beetle species, including five species of burying beetles (Nicrophorinae: Nicrophorus Fabricius, 1775) and three species from the Silphinae subfamily. Consistent with previous work, all species showed seasonal variation in abundance, with peak abundance of most species occurring between June and August. All but one species (Nicrophorus sayi Laporte, 1840) showed positive relationships between abundance and temperature. We find evidence consistent with seasonal partitioning of resources among Nicrophorus habitat generalists that could potentially reduce competition for limited carrion resources. In contrast, we find little evidence for seasonal differences in abundance among Nicrophorus habitat specialists, which instead may partition resources spatially. Overall, our results provide evidence consistent with an important role for seasonal resource partitioning among carrion beetle species that show higher levels of spatial (habitat) overlap within a temperate beetle guild.

scholarly journals A permutation test to analyse systematic bias and random measurement errors of medical devices via boosting location and scale models

2015 ◽  
Vol 26 (3) ◽  
pp. 1443-1460 ◽  
Author(s):  
Andreas Mayr ◽  
Matthias Schmid ◽  
Annette Pfahlberg ◽  
Wolfgang Uter ◽  
Olaf Gefeller
Keyword(s):  
Random Error ◽  
Measurement Errors ◽  
Large Scale ◽  
Permutation Test ◽  
Generalized Additive Models ◽  
Skin Pigmentation ◽  
Test Procedure ◽  
Additive Models ◽  
Systematic Bias ◽  
Boosting Algorithm

Measurement errors of medico-technical devices can be separated into systematic bias and random error. We propose a new method to address both simultaneously via generalized additive models for location, scale and shape (GAMLSS) in combination with permutation tests. More precisely, we extend a recently proposed boosting algorithm for GAMLSS to provide a test procedure to analyse potential device effects on the measurements. We carried out a large-scale simulation study to provide empirical evidence that our method is able to identify possible sources of systematic bias as well as random error under different conditions. Finally, we apply our approach to compare measurements of skin pigmentation from two different devices in an epidemiological study.

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