scholarly journals High-Resolution Spatial Distribution of Bird Movements Estimated from a Weather Radar Network

2020 ◽  
Vol 12 (4) ◽  
pp. 635 ◽  
Author(s):  
Bart Kranstauber ◽  
Willem Bouten ◽  
Hidde Leijnse ◽  
Berend-Christiaan Wijers ◽  
Liesbeth Verlinden ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Scales ◽  
Regional Scale ◽  
Habitat Associations ◽  
Fine Scale ◽  
Migration Patterns ◽  
Spatial Image ◽  
Weather Radars ◽  
Spatial Coverage ◽  
Radar Imagery

Weather radars provide detailed information on aerial movements of organisms. However, interpreting fine-scale radar imagery remains challenging because of changes in aerial sampling altitude with distance from the radar. Fine-scale radar imagery has primarily been used to assess mass exodus at sunset to study stopover habitat associations. Here, we present a method that enables a more intuitive integration of information across elevation scans projected in a two-dimensional spatial image of fine-scale radar reflectivity. We applied this method on nights of intense bird migration to demonstrate how the spatial distribution of migrants can be explored at finer spatial scales and across multiple radars during the higher flying en-route phase of migration. The resulting reflectivity maps enable explorative analysis of factors influencing their regional and fine-scale distribution. We illustrate the method’s application by generating time-series of composites of up to 20 radars, achieving a nearly complete spatial coverage of a large part of Northwest Europe. These visualizations are highly useful in interpreting regional-scale migration patterns and provide detailed information on bird movements in the landscape and aerial environment.

scholarly journals STATUS OF THE INVASION OF A POACEAE SPECIES IN TROPICAL SEMIARID RESERVOIRS

2017 ◽  
Vol 35 (0) ◽  
Author(s):  
R.M.A. ALVES ◽  
M.B. ALBUQUERQUE ◽  
L.G. BARBOSA
Keyword(s):  
Spatial Distribution ◽  
Native Species ◽  
Spatial Scales ◽  
Regional Scale ◽  
Statistical Computing ◽  
Relevant Factor ◽  
R Program ◽  
R Project ◽  
Environmental Instability ◽  
Biotic Acceptance

ABSTRACT The species of the Urochloa genus, exotic and infesting in Brazilian waters, are known to be invasive and dominant, occupying from humid, shallow areas, and irrigation canals to margins of deep reservoirs. This paper hypothesis that less depth reservoirs have higher infestation rate and higher biomass of U. arrecta. The objectives were to measure the percentage of occurrence of exotic macrophyte U. arrecta in 40 ecosystems from the Mamanguape basin (Paraíba, Brazil) and determine the infestation of the species in two reservoirs. The acquired data were geo-referenced with the ArcGIS software (v. 9.3). A covariance analysis was performed using the R program (The R project is Statistical Computing). The results showed large spatial distribution of the species, indicating that reservoirs may act as steppingstones in the landscape, in a regional scale. The hypothesis of biotic acceptance is seen as a relevant factor in explaining the presence of the species with low percentage of occurrence in 37 out of the 40 sampled ecosystems, being observed only in areas prone to the colonization of native and naturalized macrophytes, in banks and points of lower declivity, in both spatial scales studied. Thus, factors such as environmental instability (promoted by intermittent or prolonged desiccation of the habitat), shadowing and declivity of the reservoirs synergistically acted on exotic and native species.

scholarly journals Socio-demographic, not environmental, risk factors explain fine-scale spatial patterns of diarrhoeal disease in Ifanadiana, rural Madagascar

2021 ◽  
Vol 288 (1946) ◽  
pp. 20202501
Author(s):  
Michelle V. Evans ◽  
Matthew H. Bonds ◽  
Laura F. Cordier ◽  
John M. Drake ◽  
Felana Ihantamalala ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Patterns ◽  
Disease Risk ◽  
Spatial Scales ◽  
Explanatory Power ◽  
Diarrhoeal Disease ◽  
Local Health ◽  
Fine Scale ◽  
Local Public Health ◽  
Precision Health

Precision health mapping is a technique that uses spatial relationships between socio-ecological variables and disease to map the spatial distribution of disease, particularly for diseases with strong environmental signatures, such as diarrhoeal disease (DD). While some studies use GPS-tagged location data, other precision health mapping efforts rely heavily on data collected at coarse-spatial scales and may not produce operationally relevant predictions at fine enough spatio-temporal scales to inform local health programmes. We use two fine-scale health datasets collected in a rural district of Madagascar to identify socio-ecological covariates associated with childhood DD. We constructed generalized linear mixed models including socio-demographic, climatic and landcover variables and estimated variable importance via multi-model inference. We find that socio-demographic variables, and not environmental variables, are strong predictors of the spatial distribution of disease risk at both individual and commune-level (cluster of villages) spatial scales. Climatic variables predicted strong seasonality in DD, with the highest incidence in colder, drier months, but did not explain spatial patterns. Interestingly, the occurrence of a national holiday was highly predictive of increased DD incidence, highlighting the need for including cultural factors in modelling efforts. Our findings suggest that precision health mapping efforts that do not include socio-demographic covariates may have reduced explanatory power at the local scale. More research is needed to better define the set of conditions under which the application of precision health mapping can be operationally useful to local public health professionals.

Habitat use of Orconectes meeki meeki and Orconectes williamsi in an intermittent Ozark stream

2015 ◽  
Vol 21 (1) ◽  
pp. 103-114 ◽  
Author(s):  
Shawna S. Herleth-King ◽  
Hayden T. Mattingly ◽  
Robert J. DiStefano
Keyword(s):  
Habitat Use ◽  
Spatial Scales ◽  
Habitat Associations ◽  
Surface Velocity ◽  
Fine Scale ◽  
Arkansas River ◽  
Surface Flows ◽  
River Drainage ◽  
White River ◽  
Hyporheic Zones

Abstract Relatively few studies have examined fine-scale habitat use of crayfish in headwater streams, despite increasing awareness of the importance of such habitats. Orconectes meeki meeki, Meek’s crayfish, is endemic to the upper White River drainage of Missouri and Arkansas, and Orconectes williamsi, Williams’ crayfish, occurs only there and in the Arkansas River drainage. Our objective was to describe speciesspecific habitat use of these crayfishes at two spatial scales (pool-riffle and microhabitat) within a seasonally intermittent stream. From May through August 2008, we sampled ten riffles and five pools in Rock Creek, Missouri, to quantify surface and hyporheic environmental variables. Density of O. m. meeki was similar between riffles and pools; O. williamsi density was greater in riffles. At the riffle scale, O. m. meeki was positively associated with wetted channel width and upwelling hyporheic zones; O. williamsi was negatively associated with surface and hyporheic water temperatures. At the microhabitat scale within riffles, O. m. meeki was positively associated with wetted depth and pebble-cobble substrates; O. williamsi was positively associated with surface velocity and pebble-cobble substrates. Habitat use was relatively static for both species as surface flows waned from May through August. Our research provides detailed, fine-scale habitat associations of the crayfish to complement existing knowledge of these species at coarser spatial scales.

Multi-scale habitat associations of the black-footed rock-wallaby in north-western South Australia

2017 ◽  
Vol 44 (3) ◽  
pp. 207
Author(s):  
Laura Ruykys
Keyword(s):  
Vegetation Type ◽  
Spatial Scales ◽  
Regional Scale ◽  
Habitat Type ◽  
South Australia ◽  
Habitat Associations ◽  
Coarse Grained ◽  
Scale Parameters ◽  
Multi Scale ◽  
Core Areas

Context Research on species’ habitat associations is strengthened if it combines coarse-grained landscape data with finer-scale parameters. However, due to the effort required to measure fine-scale parameters, studies on threatened species that unite these two scales remain relatively rare. Aim This study aimed to undertake a multi-scale analysis of the habitat association of the threatened Petrogale lateralis (MacDonnell Ranges race) in the Anangu Pitjantjatjara Yankunytjatjara (APY) Lands, South Australia. Method Analyses were conducted at four spatial scales: (1) across the Central Ranges IBRA Region (regional scale); (2) on hills in the APY Lands at which P. lateralis is extant and extinct (site scale); (3) at ‘core’ and ‘non-core’ areas within those hills (hillside scale); and (4) at rocky refuges. The maximum entropy approach through the software MaxEnt was used for the analysis at the regional scale. At the remaining scales, fieldwork was used to collect, and regression modelling to analyse, data. Key results At the regional scale, presence was associated with slope and geology. At the site scale, aspect, rock abundance and habitat type are likely to have facilitated animal persistence at extant sites. At the hillside scale, the aspect, vegetation type and rock complexity of core areas are likely to have contributed to their higher use. Size, exposure and accessibility were significant predictors of the use of rocky refuges. Conclusions All four spatial scales yielded novel information on the habitat associations of P. lateralis, supporting previous researchers’ suggestions that habitat modelling should be conducted at multiple spatial scales. Implications The study exemplifies the utility of combining MaxEnt modelling with fieldwork-derived data. The results may have conservation implications for this threatened race, and may also provide a model for other studies of faunal habitat associations.

scholarly journals Repeated surveying over 6 years reveals that fine-scale habitat variables are key to tropical mountain ant assemblage composition and functional diversity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mulalo M. Muluvhahothe ◽  
Grant S. Joseph ◽  
Colleen L. Seymour ◽  
Thinandavha C. Munyai ◽  
Stefan H. Foord
Keyword(s):  
Climate Change ◽  
Species Composition ◽  
High Altitude ◽  
Functional Diversity ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Abiotic Factors ◽  
Composition Analysis ◽  
Fine Scale

AbstractHigh-altitude-adapted ectotherms can escape competition from dominant species by tolerating low temperatures at cooler elevations, but climate change is eroding such advantages. Studies evaluating broad-scale impacts of global change for high-altitude organisms often overlook the mitigating role of biotic factors. Yet, at fine spatial-scales, vegetation-associated microclimates provide refuges from climatic extremes. Using one of the largest standardised data sets collected to date, we tested how ant species composition and functional diversity (i.e., the range and value of species traits found within assemblages) respond to large-scale abiotic factors (altitude, aspect), and fine-scale factors (vegetation, soil structure) along an elevational gradient in tropical Africa. Altitude emerged as the principal factor explaining species composition. Analysis of nestedness and turnover components of beta diversity indicated that ant assemblages are specific to each elevation, so species are not filtered out but replaced with new species as elevation increases. Similarity of assemblages over time (assessed using beta decay) did not change significantly at low and mid elevations but declined at the highest elevations. Assemblages also differed between northern and southern mountain aspects, although at highest elevations, composition was restricted to a set of species found on both aspects. Functional diversity was not explained by large scale variables like elevation, but by factors associated with elevation that operate at fine scales (i.e., temperature and habitat structure). Our findings highlight the significance of fine-scale variables in predicting organisms’ responses to changing temperature, offering management possibilities that might dilute climate change impacts, and caution when predicting assemblage responses using climate models, alone.

scholarly journals Spatio-temporal distribution patterns of Plutella xylostella (Lepidoptera: Plutellidae) in a fine-scale agricultural landscape based on geostatistical analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jian-Yu Li ◽  
Yan-Ting Chen ◽  
Meng-Zhu Shi ◽  
Jian-Wei Li ◽  
Rui-Bin Xu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Plutella Xylostella ◽  
Agricultural Landscape ◽  
Seasonal Pattern ◽  
Distribution Patterns ◽  
Geostatistical Analysis ◽  
Distribution Model ◽  
Detailed Knowledge ◽  
Fine Scale ◽  
Spatio Temporal

AbstractA detailed knowledge on the spatial distribution of pests is crucial for predicting population outbreaks or developing control strategies and sustainable management plans. The diamondback moth, Plutella xylostella, is one of the most destructive pests of cruciferous crops worldwide. Despite the abundant research on the species’s ecology, little is known about the spatio-temporal pattern of P. xylostella in an agricultural landscape. Therefore, in this study, the spatial distribution of P. xylostella was characterized to assess the effect of landscape elements in a fine-scale agricultural landscape by geostatistical analysis. The P. xylostella adults captured by pheromone-baited traps showed a seasonal pattern of population fluctuation from October 2015 to September 2017, with a marked peak in spring, suggesting that mild temperatures, 15–25 °C, are favorable for P. xylostella. Geostatistics (GS) correlograms fitted with spherical and Gaussian models showed an aggregated distribution in 21 of the 47 cases interpolation contour maps. This result highlighted that spatial distribution of P. xylostella was not limited to the Brassica vegetable field, but presence was the highest there. Nevertheless, population aggregations also showed a seasonal variation associated with the growing stage of host plants. GS model analysis showed higher abundances in cruciferous fields than in any other patches of the landscape, indicating a strong host plant dependency. We demonstrate that Brassica vegetables distribution and growth stage, have dominant impacts on the spatial distribution of P. xylostella in a fine-scale landscape. This work clarified the spatio-temporal dynamic and distribution patterns of P. xylostella in an agricultural landscape, and the distribution model developed by geostatistical analysis can provide a scientific basis for precise targeting and localized control of P. xylostella.

scholarly journals Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

2021 ◽  
Vol 10 (3) ◽  
pp. 186
Author(s):  
HuiHui Zhang ◽  
Hugo A. Loáiciga ◽  
LuWei Feng ◽  
Jing He ◽  
QingYun Du
Keyword(s):  
Spatial Scales ◽  
Digital Elevation Models ◽  
Regional Scale ◽  
Drainage Density ◽  
River Network ◽  
Landscape Patterns ◽  
Estimation Methods ◽  
River Networks ◽  
Flow Accumulation ◽  
Digital Elevation

Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale.

scholarly journals Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

2014 ◽  
Vol 369 (1643) ◽  
pp. 20130194 ◽  
Author(s):  
Michael D. Madritch ◽  
Clayton C. Kingdon ◽  
Aditya Singh ◽  
Karen E. Mock ◽  
Richard L. Lindroth ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Populus Tremuloides ◽  
Spatial Scales ◽  
Imaging Spectroscopy ◽  
Spectral Signature ◽  
Spectral Variation ◽  
Fine Scale ◽  
Below Ground ◽  
Landscape Scales ◽  
Spectrometer Data

Fine-scale biodiversity is increasingly recognized as important to ecosystem-level processes. Remote sensing technologies have great potential to estimate both biodiversity and ecosystem function over large spatial scales. Here, we demonstrate the capacity of imaging spectroscopy to discriminate among genotypes of Populus tremuloides (trembling aspen), one of the most genetically diverse and widespread forest species in North America. We combine imaging spectroscopy (AVIRIS) data with genetic, phytochemical, microbial and biogeochemical data to determine how intraspecific plant genetic variation influences below-ground processes at landscape scales. We demonstrate that both canopy chemistry and below-ground processes vary over large spatial scales (continental) according to aspen genotype. Imaging spectrometer data distinguish aspen genotypes through variation in canopy spectral signature. In addition, foliar spectral variation correlates well with variation in canopy chemistry, especially condensed tannins. Variation in aspen canopy chemistry, in turn, is correlated with variation in below-ground processes. Variation in spectra also correlates well with variation in soil traits. These findings indicate that forest tree species can create spatial mosaics of ecosystem functioning across large spatial scales and that these patterns can be quantified via remote sensing techniques. Moreover, they demonstrate the utility of using optical properties as proxies for fine-scale measurements of biodiversity over large spatial scales.

scholarly journals The Assessment of Hydrologic- and Flood-Induced Land Deformation in Data-Sparse Regions Using GRACE/GRACE-FO Data Assimilation

2021 ◽  
Vol 13 (2) ◽  
pp. 235
Author(s):  
Natthachet Tangdamrongsub ◽  
Michal Šprlák
Keyword(s):  
Data Assimilation ◽  
Gravity Data ◽  
Regional Scale ◽  
Vertical Motion ◽  
Vertical Displacement ◽  
Spatial Coverage ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Gravity Recovery ◽  
Central South

The vertical motion of the Earth’s surface is dominated by the hydrologic cycle on a seasonal scale. Accurate land deformation measurements can provide constructive insight into the regional geophysical process. Although the Global Positioning System (GPS) delivers relatively accurate measurements, GPS networks are not uniformly distributed across the globe, posing a challenge to obtaining accurate deformation information in data-sparse regions, e.g., Central South-East Asia (CSEA). Model simulations and gravity data (from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO)) have been successfully used to improve the spatial coverage. While combining model estimates and GRACE/GRACE-FO data via the GRACE/GRACE-FO data assimilation (DA) framework can potentially improve the accuracy and resolution of deformation estimates, the approach has rarely been considered or investigated thus far. This study assesses the performance of vertical displacement estimates from GRACE/GRACE-FO, the PCRaster Global Water Balance (PCR-GLOBWB) hydrology model, and the GRACE/GRACE-FO DA approach (assimilating GRACE/GRACE-FO into PCR-GLOBWB) in CSEA, where measurements from six GPS sites are available for validation. The results show that GRACE/GRACE-FO, PCR-GLOBWB, and GRACE/GRACE-FO DA accurately capture regional-scale hydrologic- and flood-induced vertical displacements, with the correlation value and RMS reduction relative to GPS measurements up to 0.89 and 53%, respectively. The analyses also confirm the GRACE/GRACE-FO DA’s effectiveness in providing vertical displacement estimates consistent with GRACE/GRACE-FO data while maintaining high-spatial details of the PCR-GLOBWB model, highlighting the benefits of GRACE/GRACE-FO DA in data-sparse regions.

Using deep learning to nowcast the spatial coverage of convection from Himawari-8 satellite data

2021 ◽  
Author(s):  
Ryan Lagerquist ◽  
Jebb Q. Stewart ◽  
Imme Ebert-Uphoff ◽  
Christina Kumler
Keyword(s):  
Deep Learning ◽  
Satellite Data ◽  
Geographic Location ◽  
Ground Truth ◽  
Atmospheric Science ◽  
Skill Score ◽  
Time Of Day ◽  
Lead Times ◽  
Weather Radars ◽  
Spatial Coverage

AbstractPredicting the timing and location of thunderstorms (“convection”) allows for preventive actions that can save both lives and property. We have applied U-nets, a deep-learning-based type of neural network, to forecast convection on a grid at lead times up to 120 minutes. The goal is to make skillful forecasts with only present and past satellite data as predictors. Specifically, predictors are multispectral brightness-temperature images from the Himawari-8 satellite, while targets (ground truth) are provided by weather radars in Taiwan. U-nets are becoming popular in atmospheric science due to their advantages for gridded prediction. Furthermore, we use three novel approaches to advance U-nets in atmospheric science. First, we compare three architectures – vanilla, temporal, and U-net++ – and find that vanilla U-nets are best for this task. Second, we train U-nets with the fractions skill score, which is spatially aware, as the loss function. Third, because we do not have adequate ground truth over the full Himawari-8 domain, we train the U-nets with small radar-centered patches, then apply trained U-nets to the full domain. Also, we find that the best predictions are given by U-nets trained with satellite data from multiple lag times, not only the present. We evaluate U-nets in detail – by time of day, month, and geographic location – and compare to persistence models. The U-nets outperform persistence at lead times ≥ 60 minutes, and at all lead times the U-nets provide a more realistic climatology than persistence. Our code is available publicly.

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