A method of linking functional and structural connectivity analysis in urban green infrastructure network construction

Accelerated urbanization and population growth lead to the fragmentation of urban green space and loss of biodiversity. There are few studies on the integration of structural and functional connectivity to solve this problem. Our study aims to draw up a methodology to synthesize two methods of connectivity evaluation, accordingly, to construct an urban green infrastructure (UGI) network which is of great significance to maintain the stability of the urban ecosystem. Taking Beijing as a study area, we first used Morphological Spatial Pattern Analysis (MSPA) to identify the source patches, then combined with the graph theory-based landscape metrics to discuss the effect of different diffusion distances on the regional landscape connectivity and classify the importance level of the source patches. Finally, we used both least-cost path (LCP) and circuit theory to construct network and identify pinch areas in corridors for network optimization. The results show that (1) the landscape connectivity of the study area is obviously polarized. Source patches in mountain and hilly areas have good ecological bases and large areas, and the density of corridors is relatively high, which makes a large contribution to the overall landscape connectivity; Source patches in plain areas are severely fragmented, and there are only a small number of potential corridors connecting urban areas and suburban areas. (2) The UGI network is composed of 70 source patches and 148 potential corridors. The diffusion distance that is most beneficial to improve landscape connectivity is 20–25 km. (3) 6 pinch areas that are of great significance for improving the connectivity of the landscape present the coexistence of high migration resistance and large optimization potential, and urgently need to be restored first. This study provides a method to combine the structural and the functional analysis to construct a UGI network and formulate more scientifical protection strategies for planning departments.

The serial and generation intervals from SARS-CoV-2 transmission dynamics and their potential application in the epidemiology of two citrus diseases

<p>Since the start of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing the coronavirus disease 2019 (COVID-19) pandemic, the concepts of serial and generation intervals have been used as key epidemiological measures to understand the transmission dynamics of the disease. We carefully examined and repurposed these concepts to the understanding of the transmission chain and dynamics of two major citrus diseases: tristeza virus (caused by Citrus tristeza virus, CTV) and Huanglongbing (caused by <em>Candidatus</em> Liberibacter asiaticus). Following the fundamental definition of the concepts, the review delineates the transmission chain in the SARS-CoV-2 and that of CTV and CLas, pointing out their major similarities and differences. Then, it discusses estimation of the serial and generation intervals and their distributions for both plant diseases. Identification of infector-infectee tree pairs in a transmission chain within orchards is proposed through use of disease incidence data from intensive mapping, spatial pattern analysis, conditional probability, and simulation approaches. Like in SARS-CoV-2 dynamics, pre-symptomatic transmission in these two plant pathosystems is of epidemiological significance. Hence, estimation of the serial and generation interval can lay the foundations to understanding of early disease transmission dynamics, thus the implementation of vector control measures or eradication of infected trees. We hope this review motivates discussions on estimation and usage of these concepts to enhance understanding of the epidemiology of both of the herein examined citrus diseases.</p>

Spatial pattern analysis of forest trees based on the vectorial mark

Analysis of spatial patterns to describe the spatial correlation between a tree location and marks (i.e., structural variables), can reveal stand history, population dynamics, competition and symbiosis. However, most studies of spatial patterns have concentrated on tree location and tree sizes rather than on crown asymmetry especially with direct analysis among marks characterizing facilitation and competition among of trees, and thus cannot reveal the cause of the distributions of tree locations and quantitative marks. To explore the spatial correlation among quantitative and vectorial marks and their implication on population dynamics, we extracted vertical and horizontal marks (tree height and crown projection area) characterizing tree size, and a vectorial mark (crown displacement vector characterizing the crown asymmetry) using an airborne laser scanning point cloud obtained from two forest stands in Oxfordshire, UK. Quantitatively and vectorially marked spatial patterns were developed, with corresponding null models established for a significance test. We analyzed eight types of univariate and bivariate spatial patterns, after first proposing four types. The accuracy of the pattern analysis based on an algorithm-segmented point cloud was compared with that of a truly segmented point cloud. The algorithm-segmented point cloud managed to detect 70–86% of patterns correctly. The eight types of spatial patterns analyzed the spatial distribution of trees, the spatial correlation between tree size and facilitated or competitive interactions of sycamore and other species. These four types of univariate patterns jointly showed that, at smaller scales, the trees tend to be clustered, and taller, with larger crowns due to the detected facilitations among trees in the study area. The four types of bivariate patterns found that at smaller scales there are taller trees and more facilitation among sycamore and other species, while crown size is mostly homogeneous across scales. These results indicate that interspecific facilitation and competition mainly affect tree height in the study area. This work further confirms the connection of tree size with individual facilitation and competition, revealing the potential spatial structure that previously was hard to detect.

Mapping and Spatial Pattern Analysis of COVID-19 in Central Iran Using the Local Indicators of Spatial Association (LISA)

Background Using geographical analysis to identify geographical factors related to the prevalence of COVID-19 infection can affect public health policies aiming at controlling the virus. This study aimed to determine the spatial analysis of COVID-19 in Qom Province, using the local indicators of spatial association (LISA). Methods In a primary descriptive-analytical study, all individuals infected with COVID-19 in Qom Province from February 19th, 2020 to September 30th, 2020 were identified and included in the study. The spatial distribution in urban areas was determined using the Moran coefficient in geographic information systems (GIS); in addition, the spatial autocorrelation of the coronavirus in different urban districts of the province was calculated using the LISA method. Results The prevalence of COVID-19 in Qom Province was estimated to be 356.75 per 100,000 populations. The pattern of spatial distribution of the prevalence of COVID-19 in Qom was clustered. District 3 (Imam Khomeini St.) and District 6 (Imamzadeh Ebrahim St.) were set in the High-High category of LISA: a high-value area surrounded by high-value areas as the two foci of COVID-19 in Qom Province. District 1 (Bajak) of urban districts was set in the Low-High category: a low-value area surrounded by high values. This district is located in a low-value area surrounded by high values. Conclusions According to the results, district 3 (Imam Khomeini St.) and district 6 (Imamzadeh Ebrahim St.) areas are key areas for preventing and controlling interventional measures. In addition, considering the location of District 1 (Bajak) as an urban district in the Low-High category surrounded by high values, it seems that distance and spatial proximity play a major role in the spread of the disease.

Linking Activity of Common Pipistrelles, Pipistrellus Pipistrellus, in an Urbanised Area With a Nearby Mass Swarming Site

The Erňa cave, a mass winter hibernaculum and important swarming site of the common pipistrelle, Pipistrellus pipistrellus, is located in the Slovak Karst, near the Košice urban agglomeration in eastern Slovakia. Over the past two decades, the unusual behaviour of late summer or autumn accidental but abundant occurrences of this species have been observed in buildings (so-called invasions) in Košice. It has been hypothesised that these events are related to bats swarming and hibernating in the Erňa cave; however, causality has not been confirmed. We measured the relative activity of bats from the end of the breeding season through the invasions and autumn swarming prior to the onset of hibernation by recording their echolocation calls on car-based transects in order to find any spatial and temporal linkage between activity in the urban area and the swarming site. Over two years we recorded 6,253 sequences with echolocation calls of P. pipistrellus and 5,239 records of other bats along four transects totalling 7,121 km in length. Spatial pattern analysis found that the city agglomeration presented a local hotspot of the species’ activity, especially during the invasion season. Multivariate generalised additive modelling confirmed an increased density of records of P. pipistrellus between the urban area and the hibernaculum in the pre-hibernation season, whereas this pattern was not found to be consistent on the control transects near the city. Contrary to that, other bat species showed little variation in their activity between transects and seasons. The obtained results suggest that the relatively short geographical distance between the urban agglomeration and the large swarming site is likely a clue to the frequent city invasions of the species, although the role of the city as a hibernation area cannot be completely omitted.