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The environmental complex
Published in Stephen R. Gliessman, V. Ernesto Méndez, Victor M. Izzo, Eric W. Engles, Andrew Gerlicz, Agroecology, 2023
Stephen R. Gliessman, V. Ernesto Méndez, Victor M. Izzo, Eric W. Engles, Andrew Gerlicz
In a wet tropical lowland environment, where soils are poorly drained and rainfall is high, slight topographic variation can make a big difference in soil moisture and drainage. In such an area, the lower-lying areas of a field may be subject to much more waterlogging than the rest of the field, and crop plants growing there may experience arrested root development and poorer performance. Some farmers in the region of Tabasco, Mexico, plant waterlogging-tolerant crops, such as rice or local varieties of taro (Colocasia spp. or Xanthosoma spp.), in the lower-lying areas of their farms as a way of making a better match between crop requirements and field conditions. Finding ways to take advantage of the spatial heterogeneity of conditions by adjusting crop types and arrangements is often more ecologically efficient than trying to enforce homogeneity or ignore heterogeneity.
Modeling Virus Dynamics in Time and Space
Published in Ranjit Kumar Upadhyay, Satteluri R. K. Iyengar, Spatial Dynamics and Pattern Formation in Biological Populations, 2021
Ranjit Kumar Upadhyay, Satteluri R. K. Iyengar
Spatial heterogeneity: It is generally defined as the complexity and variability of the system variables in space. It is a challenge to study how spatial heterogeneity of the environment and movement of individuals have an impact on the persistence and extinction of a species/disease. To understand the impact of spatial heterogeneity of the environment and movement of individuals on the persistence and extinction of a disease, Allen et al. [5] proposed a frequency-dependent SIS reaction-diffusion model for a population in a continuous spatial habitat. When we consider spatially heterogeneous interventions, it is essential to represent the location of hosts and the pattern of transmission. The spatial heterogeneities of intervention add another layer of complexity to the system and provide a challenge for modeling [223]. Examples of spatially localized interventions include ring culling (as carried out in 2001 UK foot and mouth epidemic [135] and ring vaccination [264]), school closure [120], and local top-up vaccination campaigns. Every intervention is in some sense local and therefore spatially heterogeneous.
Impacts of urban sprawl on landscapes
Published in Ciro Gardi, Urban Expansion, Land Cover and Soil Ecosystem Services, 2017
Landscapes can be seen from many views depending on the phenomenon under consideration. For landscape ecology, focused on the understanding of the interactions between spatial heterogeneity and ecological processes, ‘a landscape is an area that is spatially heterogeneous in at least one factor of interest’ (Turner et al., 2001; Turner, 2005). Other authors insist on anthropogenic aspects: ‘A heterogeneous area comprising interacting ecosystems that are repeated in similar form throughout, including both natural and anthropogenic land cover, across which humans interact with their environment’ (Forman and Godron, 1981). For social science, landscape is understood ‘as an arena where conflicting interests meet, but also as sites of importance for people’s individual and collective memories and identifications’ (Tengberg et al., 2012). According the European Landscape Convention, ‘landscape means an area, as perceived by people, whose character is the result of the action and interaction of natural and/or human factors’ (Committee of Ministers of the Council of Europe, 2000). The convention promotes the integration of landscapes in any policies with possible direct or indirect impacts on landscapes such as cultural, environmental, agricultural, social and economic policies, using a participatory approach. That means to integrate landscape issues into spatial and urban planning policies and to develop strategies and guidelines to create, enhance, protect, restore and manage landscapes. For this contribution we have adopted the definition of the European Landscape Convention.
Spatial analysis of accidents involving food delivery motorcycles in Taiwan
Published in Transportation Planning and Technology, 2022
Pei-Chun Lin, Chung-Wei Shen, Jenhung Wang, Chuan-Ming Yang
Ziakopoulos and Yannis (2020) conduct a comprehensive evaluation of the available literature on the various spatial methodologies by which researchers address the spatial dimension in their investigations and analyses. Wang, Quddus, and Ison (2013) reviewed factors affecting road safety and found speed, congestion and road horizontal curvature to have mixed effects. The types and causes of traffic accidents are influenced by spatial variables such as geographical conditions, socioeconomic conditions, land use, and other local factors, resulting in both spatial heterogeneity and spatial dependency. Spatial heterogeneity refers to the variations between spatial units that have distinct spatial characteristics. Spatial dependency refers to the fact that different spatial units can exhibit the same spatial characteristics concurrently, which can result in spatial clustering (Fischer and Getis 2009). In traffic accident analysis, a spatial cluster is a group of spatial units with similar types and causes of accidents that may have a spillover effect on neighboring spatial units. Macro-level spatial analysis contributes to the improvement of road safety and the prevention of accidents. Adopting an aggregated approach to transportation policy that accounts for potential neighborhood implications is beneficial (Lee, Abdel-Aty, and Jiang 2014).
Bifurcation branch and stability of stationary solutions of a predator–prey model
Published in Applicable Analysis, 2022
It is well known that the spatial heterogeneity plays an important and interesting role in the population dynamics, such as the persistence, extinction and coexistence of species. To investigate the effects of spatial heterogeneity, the reaction–diffusion models with spatially variable coefficients have been widely studied [1–9]. In particular, observing that the behavior of the population model is usually sensitive to certain coefficient function becoming small in part of the underlying spatial region, the degenerate competition model [10,11] and predator–prey model [12–20] have been successfully employed to reveal the effects of spatial heterogeneity. Moreover, the transition of the behavior from a homogeneous environment to a degenerate heterogeneous environment is helpful to get a better understanding of the more natural models with variable coefficients.
Multivariate spatial patterns analysis of environmental variables and benthic metrics in five California waterbodies
Published in Journal of Environmental Science and Health, Part A, 2019
Lenwood W. Hall, Raymond W. Alden, Ronald D. Anderson, William D. Killen
The repeated use of remotely sensed imagery and geographic information systems led to the increased analysis and identification of spatial patterns over time in the field of spatial ecology.[4] These technologies have increased the ability to determine how human activities have impacted animal habitat.[5] An issue that is commonly present in spatial analysis is spatial heterogeneity where hot or cold spots are found.[6] In this case, univariate and multivariate analysis is not always static throughout a geographical area but instead exhibits anisotropy.[1] Spatial heterogeneity of populations and communities plays an important role in many ecological theories, such as theories of succession, adaptation, maintenance of species diversity, community stability, competition, predator–prey interactions, parasitism, epidemics and ergoclines.[3] In the environmental sciences, the presence of hydrophobic organic chemicals (HOCs) in aquatic sediment is a good example of spatial heterogeneity because HOCs are not expected to accumulate in all types of sediment within a waterbody but are more dominant in silt/clay or depositional areas.[7] This interest in spatial analysis also applies to environmental science research when addressing potential impacts of multiple environmental stressors (variables) on resident benthic communities (receptors).