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.

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 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.

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.

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.

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

2011 ◽  
Vol 7 (4) ◽  
pp. 1225-1246 ◽  
Author(s):  
G. Levavasseur ◽  
M. Vrac ◽  
D. M. Roche ◽  
D. Paillard ◽  
A. Martin ◽  
...  
Keyword(s):  
Air Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Generalized Additive Models ◽  
Regional Scale ◽  
Local Scale ◽  
Additive Models ◽  
Simple Relationship ◽  
Temperature Conditions

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 variability between their results. Studying a heterogeneous variable such as permafrost implies conducting analysis at a smaller spatial scale compared with climate models resolution. Our approach consists of applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local-scale permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of air temperature at the surface. Then we define permafrost distribution over Eurasia by air temperature conditions. In a first validation step on present climate (CTRL period), this method shows some limitations with non-systematic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a Multinomial Logistic GAM (ML-GAM), which directly predicts the occurrence probabilities of local-scale permafrost. The obtained permafrost distributions appear in a better agreement with CTRL data. In average for the nine PMIP2 models, we measure a global agreement with CTRL permafrost data that is better when using ML-GAM than when applying the GAM method with air temperature conditions. In both cases, the provided local information reduces the variability between climate models results. This also confirms that a simple relationship between permafrost and the air temperature only is not always sufficient to represent local-scale 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. The prediction of the SDMs (GAM and ML-GAM) is not significantly in better agreement with LGM permafrost data than large-scale fields. At the LGM, both methods do not reduce the variability between climate models results. We show that LGM permafrost distribution from climate models strongly depends on large-scale air temperature at the surface. LGM simulations from climate models lead to larger differences with LGM data than in the CTRL period. These differences reduce the contribution of downscaling.

scholarly journals A Comparison of the Influence of Vegetation Cover on the Precision of an UAV 3D Model and Ground Measurement Data for Archaeological Investigations: A Case Study of the Lepelionys Mound, Middle Lithuania

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5303
Author(s):  
Algimantas Česnulevičius ◽  
Artūras Bautrėnas ◽  
Linas Bevainis ◽  
Donatas Ovodas
Keyword(s):  
Vegetation Cover ◽  
Large Scale ◽  
3D Model ◽  
Three Dimensional ◽  
Measurement Data ◽  
Aerial Photographs ◽  
Herbaceous Vegetation ◽  
Ground Measurement ◽  
The Impact

The aim of this research was to conduct a comparative analysis of the precision of ground geodetic data versus the three-dimensional (3D) measurements from unmanned aerial vehicles (UAV), while establishing the impact of herbaceous vegetation on the UAV 3D model. Low (up to 0.5 m high) herbaceous vegetation can impede the establishment of the anthropogenic roughness of the surface. The identification of minor surface alterations, which enables the determination of their anthropogenic origin, is of utmost importance in archaeological investigations. Vegetation cover is regarded as one of the factors influencing the identification of such minor forms of relief. The research was conducted on the Lepelionys Mound (Prienai District Municipality, Lithuania). Ground measurements were obtained using Trimble GPS, and UAV “Inspire 1” was used for taking aerial photographs. Following the data from the ground measurements and aerial photographs, large scale surface maps were drawn and the errors in the measurement of the position of the isolines were compared. The results showed that the largest errors in the positional measurements of fixed objects were conditioned by the height of grass. Grass with a height of up to 0.1 m resulted in discrepancies of up to 0.5 m, whereas grass that was up to 0.5 m high led to discrepancies up to 1.3 m high.

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