Most contemporary crop yield models focus on a small time window, operate on a plot location, or do not include the effects of the changing environment, which makes it difficult to use these models to assess the agricultural sustainability for past s...
Using agricultural straw to generate electricity is an effective approach for relieving the pressure of procuring a reliable energy supply and reducing environmental pollution. Because the locations of the power plants have a significant impact on th...
Three competition indices were tested against experimental data on the growth of individual trees in mapped forest stands and outputs of spatially explicit, process-based models of competition. The comparison showed the fundamental importance of taki...
Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across lar...
This study introduces a novel spatially explicit modeling framework developed to quantify the secondary environmental benefits of groundwater protection strategies in karst landscapes, with a specific application to the Hranice Abyss region. The mode...
Soil compaction caused by field traffic is one of the main threats to agricultural landscapes. Compacted soils have a reduced hydraulic conductivity, lower plant growth and increased surface runoff resulting in numerous environmental issues such as i...
Farmland abandonment has important impacts on biodiversity and ecosystem recovery, as well as food security and rural sustainable development. Due to rapid urbanization and industrialization, farmland abandonment has become an increasingly important...
The high volumes of animal manure and sewage sludge, as a consequence of the development of intensive and specialized cattle dairy farms in peri-urban areas, pose challenges to local environmental quality and demands for systems innovation. Besides t...
We selected the COVID-19 outbreak in the state of Oregon, USA as a system for developing a general geographically nuanced epidemiological forecasting model that balances simplicity, realism, and accessibility. Using the life history simulator HexSim,...
Recently, a wide range of models have been used in analyzing the costs and benefits of land utilization in river basins. Despite these advances, there is not enough information on how to select appropriate models to perform cost-benefit analyses. A l...
Nesting habitat for the federally endangered loggerhead sea turtle (Caretta caretta) were designated as critical in 2014 for beaches along the Atlantic Coast and Gulf of Mexico. Nesting suitability is routinely determined based on site specific infor...
To predict the potential success of an invading non-native species, it is important to understand its dynamics and interactions with native species in the early stages of its invasion. In spatially implicit models, mathematical stability criteria are...
Identifying correspondences of road segments in different road networks, namely road-network matching, is an essential task for road network-centric data processing such as data integration of road networks and data quality assessment of crowd-source...
25 May 2021
This research aims to explore the spatial pattern of vulnerability and resilience to natural hazards in northeastern Taiwan. We apply the spatially explicit resilience-vulnerability model (SERV) to quantify the vulnerability and resilience to natural...
In West Africa, where the majority of the population relies on natural resources and rain-fed agriculture, regionally adapted agricultural land-use planning is increasingly important to cope with growing demand for land-use products and intensifying...
The repeated application of herbicides has led to the development of herbicide resistance. Models are useful for identifying key processes and understanding the evolution of resistance. This study developed a spatially explicit model at a landscape s...
Wildfires release the greatest amount of carbon into the atmosphere compared to other forest disturbances. To understand how current and potential future fire regimes may affect the role of the Eurasian boreal forest in the global carbon cycle, we em...
Negative impacts from aquatic invasive plants in the United States include economic costs, loss of commercial and recreational use, and environmental damage. Simulation models are valuable tools for predicting the invasion potentials of species and f...
Understanding organism movement is at the heart of many ecological disciplines. The study of landscape connectivity—the extent to which a landscape facilitates organism movement—has grown to become a central focus of spatial ecology and c...
This study used a spatially-explicit model to identify the amount and spatial distribution of economically feasible sites for establishing dedicated energy crops under various market and policy scenarios. A sensitivity analysis was performed for a bi...
It is critical that we move our understanding of the ecosystem services (ESs) produced by landscapes from the present abundance of analysis to a fundamental basis of design. This involves enhancing the ability to understand and model the interconnect...
It is increasingly common for developers to be asked to manage the impacts of their projects on biodiversity by restoring other degraded habitats that are ecologically equivalent to those that are impacted. These measures, called biodiversity offsets...
Fisheries management has historically focused on the population elasticity of target fish based primarily on demographic modeling, with the key assumptions of stability in environmental conditions and static trophic relationships. The predictive capa...
The potential for a non-native plant species to invade a new habitat depends on broadscale factors such as climate, local factors such as nutrient availability, and the biotic community of the habitat into which the plant species is introduced. We de...
Small-angle X-ray fiber diffraction has informed much of what we know regarding the molecular events during muscle contraction but robust tools for predicting X-ray fiber patterns from muscle have been lacking. A complication in formulating such tool...
Fire regimes are important components of environmental dynamics, but our understanding of them is limited. Despite recent advances in the methodologies used to remotely sense and map fires and burned areas and new case studies that shed light on loca...
Assessing spatial model performance often presents challenges related to the choice and suitability of traditional statistical methods in capturing the true validity and dynamics of the predicted outcomes. The stochastic nature of many of our contemp...
A fully coupled simulation of ecophysiological, hydrological and biochemical processes is significant for better understanding the individual and interactional impact of sophisticated land surface processes under future disturbances from nature and h...
Assessing the impact of community-based conservation programs on wildlife biodiversity remains a significant challenge. This pilot study was designed to develop and demonstrate a scalable, spatially explicit workflow using thermal infrared (TIR) imag...
Global sensitivity analysis, like variance-based methods for massive raster datasets, is especially computationally costly and memory-intensive, limiting its applicability for commodity cluster computing. The computational effort depends mainly on th...
Mapping aboveground carbon density in tropical forests can support CO2 emission monitoring and provide benefits for national resource management. Although LiDAR technology has been shown to be useful for assessing carbon density patterns, the accurac...
Addressing “wicked” problems like urban stormwater management necessitates building shared understanding among diverse stakeholders with the influence to enact solutions cooperatively. Fuzzy cognitive maps (FCMs) are participatory modeling tools that...
Selection and adaptation of individuals to their underlying environments are highly dynamical processes, encompassing interactions between the individual and its seasonally changing environment, synergistic or antagonistic interactions between indivi...
Australia is currently free of canine rabies. Spatio-ecological knowledge about dingoes in northern Australia is currently a gap that impedes the application of disease spread models and our understanding of the potential transmission of rabies, in t...
Seabed sediment composition is an important component of benthic habitat and there are many approaches for producing maps that convey sediment information to marine managers. Random Forest is a popular statistical method for thematic seabed sediment...
Globally, there are myriad situations in which people aim to conserve, restore, or manage forest ecosystems at small spatial scales of 50 ha or less. To inform management, forest dynamics models provide an increasingly diverse and valuable portfolio...
Individual-tree-based models (IBMs) have emerged to provide finer-scale operational simulations of stand dynamics by accommodating and/or representing tree-to-tree interactions and competition. Like stand-level growth model development, IBMs need an...
The establishment of enclosed conservation areas are claimed to be the driving force for the long-term survival of wildlife populations. Whilst fencing provides an important tool in conservation, it simultaneously represents a controversial matter as...
Biological invasions represent some of the most severe threats to local communities and ecosystems. Among invasive species, the vector-borne pathogen Xylella fastidiosa is responsible for a wide variety of plant diseases and has profound environmenta...
We consider the multispecies model described by a coupled system of diffusion–reaction equations, where the coupling and nonlinearity are given in the reaction part. We construct a semi-discrete form using a finite volume approximation by space...
By importing agricultural commodities, Germany causes ecological impacts in other countries. One of these impacts is the loss of biodiversity in the producing regions. This paper presents a new method that combines agricultural trade data with land c...
Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation...
In the context of the energy transition, the integration of land use considerations into energy planning can provide significant improvements. In energy system optimization models (ESOMs), land use aspects can be integrated at the cost of a finer spa...
This paper deals with crop rotation as a method to improve soil fertility and control pests from an economic point of view. It outlines a new framework for modelling of more sustainable decision-making of farmers under the auspices of ecosystem servi...
The Eagle Creek watershed, a small subbasin (125 km2) within the Maumee River Basin, Ohio, was selected as a part of the Great Lakes Restoration Initiative (GLRI) “Priority Watersheds” program to evaluate the effectiveness of agricultural...
Combining national forest inventory (NFI) data with digital site maps of high resolution enables spatially explicit predictions of site productivity. The aim of this study is to explore the possibilities and limitations of this database to analyze th...
Biochemical Oxygen Demand (BOD) is a key indicator of organic pollution and a proxy indicator reflecting organic loading that can indirectly influence eutrophication processes in aquatic systems. This study presents a spatially explicit, physically b...
(1) Background: The stochastic nature of agent-based models (ABMs) may be responsible for the variability of simulated outputs. Multiple simulation runs (i.e., replicates) need to be performed to have enough sample size for hypothesis testing and val...
Effective planning of urban heating systems is crucial for achieving net-zero emissions at the city level. In particular, the spatial dimension plays a pivotal role in shaping the design and operation of these systems. Nonetheless, the integration of...
Spatial data has distinctive properties that differentiate it from non-spatial data. One prominent characteristic is spatial autocorrelation (SA). When machine learning techniques are applied for spatial data modeling, they require spatially explicit...
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