Prioritizing road‐kill mitigation areas: A spatially explicit national‐scale model for an elusive carnivore

The present study provides a simplified framework verifying the degree of coverage and completeness of settlement maps derived from the OpenStreetMap (OSM) database at the national scale, with a possible use in official statistics. Measuring the completeness of the objects (i.e., buildings) derived from OpenStreetMap database supports its potential use in building/population censuses and other diachronic surveys, as well as administrative sources such as the register of building permits and land-use cadasters. A series of measurements at different scales are proposed and tested for Italy, in line with earlier studies. While recognizing the potential of the OpenStreetMap database for official statistics, the present work underlines the urgent need of an additional (spatially explicit) analysis overcoming the data heterogeneity and sub-optimal coverage of the OSM information source.

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Emergent vulnerability to intensive coastal anthropogenic disturbances in mangrove forests

10.1101/2021.04.17.440255 ◽

2021 ◽

Author(s):

Yangfan Li ◽

Zhen Zhang ◽

Yi Yang ◽

Yi Li

Keyword(s):

Anthropogenic Disturbance ◽

Mainland China ◽

Ecosystem Restoration ◽

Well Being ◽

Anthropogenic Disturbances ◽

Spatially Explicit ◽

Mangrove Forests ◽

National Scale ◽

Developing Regions ◽

Coastal Reclamation

Mangrove forests, as one of the most productive coastal ecosystems in tropical and subtropical areas, provide multiple valuable ecosystem services for human well-being. Mangrove coverage has been declining dramatically across much of developing regions due to extensive coastal anthropogenic disturbances such as reclamation, aquaculture, and seawall construction. As coastal human activities increase, there is urgent need to understand not only the direct loss, but also the vulnerability of mangroves to anthropogenic disturbances. In this study, we evaluated spatial pattern of mangrove vulnerability based on the conceptual framework of "Exposure-Sensitivity-Resilience" using geospatial datasets in mainland China. We find that within all 25,829 ha mangroves in five coastal provinces of mainland China in 2015, nearly 76% of mangroves was exposed or threatened by anthropogenic disturbances. Coastal reclamation and aquaculture were the key threats causing mangrove vulnerability. The overall distribution of high, medium and low vulnerability was following similar trend of aquaculture distribution, which suggests aquaculture was the greatest anthropogenic disturbance agent to mangroves. Hotspot regions for mangrove vulnerability are located at the developing provinces such as Guangxi and Hainan. This study provides the first spatially explicit evidence of the vulnerability of mangrove forests to intensive coastal anthropogenic disturbances at national scale, cloud serve as a benchmark for navigating coastal ecological redline management and coastal ecosystem restoration.

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Development and evaluation of a variably saturated flow model in the global E3SM Land Model (ELM) version 1.0

Geoscientific Model Development ◽

10.5194/gmd-11-4085-2018 ◽

2018 ◽

Vol 11 (10) ◽

pp. 4085-4102 ◽

Cited By ~ 2

Author(s):

Gautam Bisht ◽

William J. Riley ◽

Glenn E. Hammond ◽

David M. Lorenzetti

Keyword(s):

Soil Moisture ◽

Flow Model ◽

Scale Model ◽

Richards Equation ◽

Benchmark Problems ◽

Spatially Explicit ◽

Grid Cells ◽

Saturated Flow ◽

Near Surface ◽

Computational Performance

Abstract. Improving global-scale model representations of near-surface soil moisture and groundwater hydrology is important for accurately simulating terrestrial processes and predicting climate change effects on water resources. Most existing land surface models, including the default E3SM Land Model (ELMv0), which we modify here, routinely employ different formulations for water transport in the vadose and phreatic zones. Clark et al. (2015) identified a variably saturated Richards equation flow model as an important capability for improving simulation of coupled soil moisture and shallow groundwater dynamics. In this work, we developed the Variably Saturated Flow Model (VSFM) in ELMv1 to unify the treatment of soil hydrologic processes in the unsaturated and saturated zones. VSFM was tested on three benchmark problems and results were evaluated against observations and an existing benchmark model (PFLOTRAN). The ELMv1-VSFM's subsurface drainage parameter, fd, was calibrated to match an observationally constrained and spatially explicit global water table depth (WTD) product. Optimal spatially explicit fd values were obtained for 79 % of global 1.9∘ × 2.5∘ grid cells, while the remaining 21 % of global grid cells had predicted WTD deeper than the observationally constrained estimate. Comparison with predictions using the default fd value demonstrated that calibration significantly improved predictions, primarily by allowing much deeper WTDs. Model evaluation using the International Land Model Benchmarking package (ILAMB) showed that improvements in WTD predictions did not degrade model skill for any other metrics. We evaluated the computational performance of the VSFM model and found that the model is about 30 % more expensive than the default ELMv0 with an optimal processor layout. The modular software design of VSFM not only provides flexibility to configure the model for a range of problem setups but also allows for building the model independently of the ELM code, thus enabling straightforward testing of the model's physics against other models.

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Earthworm distributions are not driven by measurable soil properties. Do they really indicate soil quality?

PLoS ONE ◽

10.1371/journal.pone.0241945 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0241945

Author(s):

Mark E. Hodson ◽

Ron Corstanjeb ◽

David T. Jones ◽

Jo Witton ◽

Victoria J. Burton ◽

...

Keyword(s):

Soil Quality ◽

Spatially Explicit ◽

Sampling Point ◽

Biological Factors ◽

National Scale ◽

Environmental Correlates ◽

Farm Scale ◽

Spatial Level ◽

Abundance And Distribution ◽

Earthworm Populations

Abundance and distribution of earthworms in agricultural fields is frequently proposed as a measure of soil quality assuming that observed patterns of abundance are in response to improved or degraded environmental conditions. However, it is not clear that earthworm abundances can be directly related to their edaphic environment, as noted in Darwin’s final publication, perhaps limiting or restricting their value as indicators of ecological quality in any given field. We present results from a spatially explicit intensive survey of pastures within United Kingdom farms, looking for the main drivers of earthworm density at a range of scales. When describing spatial variability of both total and ecotype-specific earthworm abundance within any given field, the best predictor was earthworm abundance itself within 20–30 m of the sampling point; there were no consistent environmental correlates with earthworm numbers, suggesting that biological factors (e.g. colonisation rate, competition, predation, parasitism) drive or at least significantly modify earthworm distributions at this spatial level. However, at the national scale, earthworm abundance is well predicted by soil nitrate levels, density, temperature and moisture content, albeit not in a simple linear fashion. This suggests that although land can be managed at the farm scale to promote earthworm abundance and the resulting soil processes that deliver ecosystem services, within a field, earthworm distributions will remain patchy. The use of earthworms as soil quality indicators must therefore be carried out with care, ensuring that sufficient samples are taken within field to take account of variability in earthworm populations that is unrelated to soil chemical and physical properties.

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Uncertainty analysis of a spatially explicit annual water-balance model: case study of the Cape Fear basin, North Carolina

Hydrology and Earth System Sciences ◽

10.5194/hess-19-839-2015 ◽

2015 ◽

Vol 19 (2) ◽

pp. 839-853 ◽

Cited By ~ 46

Author(s):

P. Hamel ◽

A. J. Guswa

Keyword(s):

Land Use ◽

North Carolina ◽

Scale Model ◽

Balance Model ◽

Water Yield ◽

Spatially Explicit ◽

Model Predictions ◽

Cape Fear ◽

Annual Water

Abstract. There is an increasing demand for assessment of water provisioning ecosystem services. While simple models with low data and expertise requirements are attractive, their use as decision-aid tools should be supported by uncertainty characterization. We assessed the performance of the InVEST annual water yield model, a popular tool for ecosystem service assessment based on the Budyko hydrological framework. Our study involved the comparison of 10 subcatchments ranging in size and land-use configuration, in the Cape Fear basin, North Carolina. We analyzed the model sensitivity to climate variables and input parameters, and the structural error associated with the use of the Budyko framework, a lumped (catchment-scale) model theory, in a spatially explicit way. Comparison of model predictions with observations and with the lumped model predictions confirmed that the InVEST model is able to represent differences in land uses and therefore in the spatial distribution of water provisioning services. Our results emphasize the effect of climate input errors, especially annual precipitation, and errors in the ecohydrological parameter Z, which are both comparable to the model structure uncertainties. Our case study supports the use of the model for predicting land-use change effect on water provisioning, although its use for identifying areas of high water yield will be influenced by precipitation errors. While some results are context-specific, our study provides general insights and methods to help identify the regions and decision contexts where the model predictions may be used with confidence.

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Regional simulation of long-term organic carbon stock changes in cropland soils using the DNDC model: 1. Large-scale model validation against a spatially explicit data set

Soil Use and Management ◽

10.1111/j.1475-2743.2006.00045.x ◽

2006 ◽

Vol 22 (4) ◽

pp. 342-351 ◽

Cited By ~ 17

Author(s):

S. Sleutel ◽

S. De Neve ◽

D. Beheydt ◽

C. Li ◽

G. Hofman

Keyword(s):

Large Scale ◽

Scale Model ◽

Spatially Explicit ◽

Dndc Model ◽

Data Set ◽

Large Scale Model ◽

Regional Simulation ◽

Spatially Explicit Data ◽

Organic Carbon Stock

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A National-Scale 1-km Resolution PM2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model

Remote Sensing ◽

10.3390/rs13183657 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3657

Author(s):

Chau-Ren Jung ◽

Wei-Ting Chen ◽

Shoji F. Nakayama

Keyword(s):

Random Forest ◽

Cross Validation ◽

Model Performance ◽

Random Forest Model ◽

Scale Model ◽

National Scale ◽

Two Stage ◽

Forest Model ◽

Monsoon Area ◽

Fold Cross Validation

Satellite-based models for estimating concentrations of particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) have seldom been developed in islands with complex topography over the monsoon area, where the transport of PM2.5 is influenced by both the synoptic-scale winds and local-scale circulations compared with the continental regions. We validated Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) with ground observations in Japan and developed a 1-km-resolution national-scale model between 2011 and 2016 to estimate daily PM2.5 concentrations. A two-stage random forest model integrating MAIAC AOD with meteorological variables and land use data was applied to develop the model. The first-stage random forest model was used to impute the missing AOD values. The second-stage random forest model was then utilised to estimate ground PM2.5 concentrations. Ten-fold cross-validation was performed to evaluate the model performance. There was good consistency between MAIAC AOD and ground truth in Japan (correlation coefficient = 0.82 and 74.62% of data falling within the expected error). For model training, the model showed a training coefficient of determination (R2) of 0.98 and a root mean square error (RMSE) of 1.22 μg/m3. For the 10-fold cross-validation, the cross-validation R2 and RMSE of the model were 0.86 and 3.02 μg/m3, respectively. A subsite validation was used to validate the model at the grids overlapping with the AERONET sites, and the model performance was excellent at these sites with a validation R2 (RMSE) of 0.94 (1.78 μg/m3). Additionally, the model performance increased as increased AOD coverage. The top-ten important predictors for estimating ground PM2.5 concentrations were day of the year, temperature, AOD, relative humidity, 10-m-height zonal wind, 10-m-height meridional wind, boundary layer height, precipitation, surface pressure, and population density. MAIAC AOD showed high retrieval accuracy in Japan. The performance of the satellite-based model was excellent, which showed that PM2.5 estimates derived from the model were reliable and accurate. These estimates can be used to assess both the short-term and long-term effects of PM2.5 on health outcomes in epidemiological studies.

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Human influences shape the first spatially explicit national estimate of urban unowned cat abundance

Scientific Reports ◽

10.1038/s41598-021-99298-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jennifer L. McDonald ◽

Elizabeth Skillings

Keyword(s):

Spatial Scales ◽

Credible Interval ◽

Common Element ◽

Spatially Explicit ◽

Urban Landscapes ◽

National Scale ◽

Public Records ◽

Mediated Effects ◽

Fields Of Study ◽

Abundance And Distribution

AbstractGlobally, unowned cats are a common element of urban landscapes, and the focus of diverse fields of study due to welfare, conservation and public health concerns. However, their abundance and distribution are poorly understood at large spatial scales. Here, we use an Integrated Abundance Model to counter biases that are inherent in public records of unowned cat sightings to assess important drivers of their abundance from 162 sites across five urban towns and cities in England. We demonstrate that deprivation indices and human population densities contribute to the number of unowned cats. We provide the first spatially explicit estimates of expected distributions and abundance of unowned cats across a national scale and estimate the total UK urban unowned cat population to be 247,429 (95% credible interval: 157,153 to 365,793). Our results provide a new baseline and approach for studies on unowned cats and links to the importance of human-mediated effects.

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Modeling Within-Person Variance in Reaction Time Data of Older Adults

GeroPsych ◽

10.1024/1662-9647/a000045 ◽

2011 ◽

Vol 24 (4) ◽

pp. 169-176 ◽

Cited By ~ 4

Author(s):

Philippe Rast ◽

Daniel Zimprich

Keyword(s):

Reaction Time ◽

Cognitive Aging ◽

Reaction Times ◽

Reaction Time Task ◽

Reaction Time Data ◽

Scale Model ◽

Time Data ◽

Time Task ◽

The Mean ◽

Second Wave

In order to model within-person (WP) variance in a reaction time task, we applied a mixed location scale model using 335 participants from the second wave of the Zurich Longitudinal Study on Cognitive Aging. The age of the respondents and the performance in another reaction time task were used to explain individual differences in the WP variance. To account for larger variances due to slower reaction times, we also used the average of the predicted individual reaction time (RT) as a predictor for the WP variability. Here, the WP variability was a function of the mean. At the same time, older participants were more variable and those with better performance in another RT task were more consistent in their responses.

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