Spatial associations between plants and vegetation community characteristics provide insights into the processes influencing plant rarity

Determining the drivers of plant rarity is a major challenge in ecology. Analysing spatial associations between different plant species can provide an exploratory avenue for understanding the ecological drivers of plant rarity. Here, we examined the different types of spatial associations between rare and common plants to determine if they influence the occurrence patterns of rare species. We completed vegetation surveys at 86 sites in woodland, forest, and heath communities in south-east Australia. We also examined two different rarity measures to quantify how categorisation criteria affected our results. Rare species were more likely to have positive associations with both rare and common species across all three vegetation communities. However, common species had positive or negative associations with rare and other common species, depending on the vegetation community in which they occurred. Rare species were positively associated with species diversity in forest communities. In woodland communities, rare species were associated negatively with species diversity but positively associated with species evenness. Rare species with high habitat specificity were more clustered spatially than expected by chance. Efforts to understand the drivers of plant rarity should use rarity definitions that consider habitat specificity. Our findings suggest that examining spatial associations between plants can help understand the drivers of plant rarity.

Quantifying spatial associations between effective green spaces and cardiovascular and cerebrovascular diseases by applying volunteered geo-referenced data

Among the top public health risks, cardiovascular and cerebrovascular diseases cause more than 1 million deaths annually globally. Urban green spaces are considered have close associations with cardiovascular and cerebrovascular diseases. However, ignoring the spatial heterogeneity of different urban green space types and considering only the configuration or compositions of urban green spaces has resulted in inconsistent and contradictory conclusions. Therefore, by introducing Tencent urban density data, four effective green spaces (EGSs) were categorized. Category 1 EGSs, which exhibit a high increasing of visitors and areas, accounted for the smallest areal percentage (0.81%). Category 2 EGSs, which exhibit a low increasing of visiting and high increasing of areas, accounted for the highest areal percentage (42.51%). Category 3 EGSs, which exhibit a high increasing of visiting and low increasing of areas, accounted for 13.70% of the total EGS areas. Category 4 EGSs, which exhibit a low increasing of visiting and areas, accounted for 3.75% of the total EGS areas. Using a geographically weighted regression model, spatial associations between EGS and cardiovascular and cerebrovascular diseases were quantified. Consequently, these spatial associations varied among EGS types and seasons. EGS configurations (perimeters of vegetation and areas of vegetation) have a more significant association with cardiovascular and cerebrovascular diseases than the composition (NDVI) of EGS. Spatial associations implying stronger relationships were observed in EGS1. The strongest association was found in summer. Enlarge the coverage of evergreen vegetation in all EGS is first considered to enhance the negative association between EGS and chronic diseases. A methodology framework was provided to classify urban green space types using multi-source data. Suggestions for how to plan different urban green spaces for developing sustainable cities have been provided in this study, which offer scientific support to urban managers and planners for effective decision making.

Impacts of Study Area Spatial Form on Co-location Quotient Analysis: Evidence From Medical Resources Distribution in Wuhan City

BackgroundThere is a sharp contradiction between supply and demand of medical resources in provincial capitals of China. Understanding the spatial patterns of medical resources and identifying their spatial association and heterogeneity is a prerequisite to ensuring limited resources are allocated fairly and optimally, which, along with improvements to urban residents’ quality of life, is a key aim of healthy city planning. MethodLocalised co-location quotient (LCLQ) analysis has been used successfully to measure directional spatial associations and heterogeneity between categorical point data. Using point of interest data and the LCLQ method, this paper presents the first analysis of spatial patterns and directional spatial associations between six medical resources across Wuhan city, and evaluates the impact of study area spatial form (considered as a new dimension of spatial scale) on spatial analysis. The unique morphology of Wuhan city, which is bisected by the Yangtze River, is used to assess the impacts on LCLQ analysis and the seeking behaviour of medical resources.ResultsThis paper demonstrated the impacts of the spatial form of a study area on the global and local values of LCLQ of local-level medical resources. When splitting the city into multiple data sets (e.g. regions A and B in this paper), the global and local LCLQ values for pharmacies, clinics and community hospitals changed signficantly in both regions after the spatial partition. The border areas between regions A and B were influenced most.ConclusionThis paper focused on the impacts of the unique spatial form of the study area created by large-scale natural barriers. we should not ignore the impacts of the unique spatial form of the study area created by large-scale natural barriers such as mountains, rivers or lakes. The findings highlight another form of multiscale analysis in urban GIS.

Broadening the Perspective of Zero-Deforestation Interventions in Peru by Incorporating Concepts from the Global Value Chain Literature

Global narratives around the links between deforestation and agricultural commodity production have led to the application of voluntary zero-deforestation agreements between companies, governments, and civil society. The continued tropical deforestation warrants a re-examination of this approach in order to customize its application for a particular location. Our paper contributes to this by exploring the spatial associations between deforestation and the production of cacao, coffee, and oil palm in the Amazon region in Peru. The geographical overlaps between deforestation, and the distribution of these commodity crops, indicate four types of spatial associations: (1) a high degree of deforestation and a high degree of commodity production (high-high); (2) a high degree of deforestation and a low degree of commodity production (high-low); (3) a low degree of deforestation and a high degree of commodity production (low-high); and (4) a low degree of deforestation and a low degree of commodity production (low-low). On the basis of these associations, we present four scenarios in which zero-deforestation supply chain interventions may operate in Peru and argue that broadening the perspective of such interventions by adopting a global value chain lens can improve the use of previously deforested lands, prevent unintended or future deforestation and, in turn, ensure that no forest area is left behind.

EXPRESS: Remembering spatial words: Sensorimotor simulation affects verbal recognition memory

Previous evidence shows that words with implicit spatial meaning or metaphorical spatial associations are perceptually simulated and can guide attention to associated locations (e.g., bird – upward location). In turn, simulated representations interfere with visual perception at an associated location. The present study investigates the effect of spatial associations on short-term verbal recognition memory to disambiguate between modal and amodal accounts of spatial interference effects across two experiments. Participants in both experiments encoded words presented in congruent and incongruent locations. Congruent and incongruent locations were based on an independent norming task. In Experiment 1, an auditorily presented word probed participants’ memory as they were visually cued to either the original location of the probe word or a diagonal location at retrieval. In Experiment 2, there was no cue at retrieval but a neutral encoding condition in which words normed to central locations were shown. Results show that spatial associations affected memory performance although spatial information was neither relevant nor necessary for successful retrieval: Words in Experiment 1 were retrieved more accurately when there was a visual cue in the congruent location at retrieval but only if they were encoded in a non-canonical position. A visual cue in the congruent location slowed down memory performance when retrieving highly imageable words. With no cue at retrieval (Experiment 2), participants were better at remembering spatially congruent words as opposed to neutral words. Results provide evidence in support of sensorimotor simulation in verbal memory and a perceptual competition account of spatial interference effect.