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Willems FM, Scheepens JF, Bossdorf O. Forest wildflowers bloom earlier as Europe warms: lessons from herbaria and spatial modelling. New Phytol 2022; 235:52-65. [PMID: 35478407 DOI: 10.1111/nph.18124] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
Today plants often flower earlier due to climate warming. Herbarium specimens are excellent witnesses of such long-term changes. However, the magnitude of phenological shifts may vary geographically, and the data are often clustered. Therefore, large-scale analyses of herbarium data are prone to pseudoreplication and geographical biases. We studied over 6000 herbarium specimens of 20 spring-flowering forest understory herbs from Europe to understand how their phenology had changed during the last century. We estimated phenology trends with or without taking spatial autocorrelation into account. On average plants now flowered over 6 d earlier than at the beginning of the last century. These changes were strongly associated with warmer spring temperatures. Flowering time advanced 3.6 d per 1°C warming. Spatial modelling showed that, in some parts of Europe, plants flowered earlier or later than expected. Without accounting for this, the estimates of phenological shifts were biased and model fits were poor. Our study indicates that forest wildflowers in Europe strongly advanced their phenology in response to climate change. However, these phenological shifts differ geographically. This shows that it is crucial to combine the analysis of herbarium data with spatial modelling when testing for long-term phenology trends across large spatial scales.
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Affiliation(s)
- Franziska M Willems
- Plant Evolutionary Ecology, Institute of Evolution and Ecology, University of Tübingen, 72076, Tübingen, Germany
- Conservation Biology, Department of Biology, University of Marburg, 35032, Marburg, Germany
| | - J F Scheepens
- Plant Evolutionary Ecology, Faculty of Biological Sciences, Goethe University Frankfurt, 60438, Frankfurt am Main, Germany
| | - Oliver Bossdorf
- Plant Evolutionary Ecology, Institute of Evolution and Ecology, University of Tübingen, 72076, Tübingen, Germany
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Ashraf MT, Dey K. Application of Bayesian Space-Time interaction models for Deer-Vehicle crash hotspot identification. Accid Anal Prev 2022; 171:106646. [PMID: 35390699 DOI: 10.1016/j.aap.2022.106646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 06/14/2023]
Abstract
The objective of this research was to identify and prioritize deer-vehicle crash (DVC) hotspots using five years of crash data. This study applied Bayesian spatiotemporal models for the identification of the DVC hotspots. The Bayesian spatiotemporal model allows to observe area-specific trends in the DVC data and highlights specific locations where DVC occurrence is deteriorating or improving over time. Census Tracts (CTs) were used as the geographic units to aggregate DVC, land use, and transportation infrastructure related data of Minnesota (MN) for the year 2015 to 2019. Several tests were conducted to evaluate the performance of the hotspot identification methods. The result showed that Type-I spatiotemporal interaction model (Model-2) outperforms other four space-time models in terms of predicting DVC frequency in CTs and hotspot identification performance test measures. Results showed that forest area, vegetation, and wetland percentages were positively associated with DVC frequency, whereas the percentage of developed land use was negatively associated with DVC frequency. The findings of this study suggest that the deer population plays an important role in DVCs, which indicates that deer population management is necessary to minimize the DVC risks. Using the final Type-I spatiotemporal interaction model, 65 "High-High" CTs were identified, where both the posterior mean of the decision parameter (potential for safety improvement) and the area-specific trend were higher. The distribution of the identified hotspots showed that the risk of DVCs was more in suburban areas with mixed land use conditions. These CTs represent high-risk zones, which need immediate safety improvement measures to reduce the DVC risks. As DVC can occur at any roadway segment location, DVC hotspots information is important for safety engineers and policymakers to implement area specific countermeasures.
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Affiliation(s)
- Md Tanvir Ashraf
- Department of Civil and Environmental Engineering, West Virginia University, Morgantown, WV 26505, USA
| | - Kakan Dey
- Department of Civil and Environmental Engineering, West Virginia University, Morgantown, WV 26505, USA.
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Amegbor PM, Borges SS, Pysklywec A, Sabel CE. Effect of individual, household and regional socioeconomic factors and PM 2.5 on anaemia: A cross-sectional study of sub-Saharan African countries. Spat Spatiotemporal Epidemiol 2022; 40:100472. [PMID: 35120685 DOI: 10.1016/j.sste.2021.100472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 10/28/2021] [Accepted: 11/29/2021] [Indexed: 11/23/2022]
Abstract
There is limited knowledge on the effect of contextual and environmental factors on the risk of anaemia, as well as the spatial distribution of anaemia in the Sub-Saharan Africa region. In this study, we used multi-country data from the Demographic & Health survey (DHS) with 270,011 observations and PM2.5 data from NASA, applied to the spatial risk pattern of anaemia in the SSA region. The prevalence of anaemia amongst women (41%) was almost twice that of men (22%). A Bayesian hierarchical model showed that individual household, neighbourhood and regional socioeconomic factors were significantly associated with the likelihood of being anaemic. 1 μg/m3 increase in cumulative lifetime PM2.5 exposure accounted for 1% (β = 0.011, CI = 0.008 - 0.015) increase in the likelihood of being anaemic. The results suggest the need for a multidimensional approach to tackle anaemia in the Sub-Saharan African region and identify high-risk areas for target intervention policies or programs.
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Aheto JMK, Utuama OA, Dagne GA. Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012-2016 SEER data. BMC Cancer 2021; 21:508. [PMID: 33957887 PMCID: PMC8101113 DOI: 10.1186/s12885-021-08254-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 04/26/2021] [Indexed: 03/17/2023] Open
Abstract
Background Prostate cancer (CaP) cases are high in the United States. According to the American Cancer Society, there are an estimated number of 174,650 CaP new cases in 2019. The estimated number of deaths from CaP in 2019 is 31,620, making CaP the second leading cause of cancer deaths among American men with lung cancer been the first. Our goal is to estimate and map prostate cancer relative risk, with the ultimate goal of identifying counties at higher risk where interventions and further research can be targeted. Methods The 2012–2016 Surveillance, Epidemiology, and End Results (SEER) Program data was used in this study. Analyses were conducted on 159 Georgia counties. The outcome variable is incident prostate cancer. We employed a Bayesian geospatial model to investigate both measured and unmeasured spatial risk factors for prostate cancer. We visualised the risk of prostate cancer by mapping the predicted relative risk and exceedance probabilities. We finally developed interactive web-based maps to guide optimal policy formulation and intervention strategies. Results Number of persons above age 65 years and below poverty, higher median family income, number of foreign born and unemployed were risk factors independently associated with prostate cancer risk in the non-spatial model. Except for the number of foreign born, all these risk factors were also significant in the spatial model with the same direction of effects. Substantial geographical variations in prostate cancer incidence were found in the study. The predicted mean relative risk was 1.20 with a range of 0.53 to 2.92. Individuals residing in Towns, Clay, Union, Putnam, Quitman, and Greene counties were at increased risk of prostate cancer incidence while those residing in Chattahoochee were at the lowest risk of prostate cancer incidence. Conclusion Our results can be used as an effective tool in the identification of counties that require targeted interventions and further research by program managers and policy makers as part of an overall strategy in reducing the prostate cancer burden in Georgia State and the United States as a whole. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-08254-0.
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Affiliation(s)
- Justice Moses K Aheto
- Department of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, P. O. Box LG13, Legon, Accra, Ghana. .,College of Public Health, University of South Florida, Tampa, USA.
| | - Ovie A Utuama
- College of Public Health, University of South Florida, Tampa, USA
| | - Getachew A Dagne
- College of Public Health, University of South Florida, Tampa, USA
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Saez M, Tobias A, Varga D, Barceló MA. Effectiveness of the measures to flatten the epidemic curve of COVID-19. The case of Spain. Sci Total Environ 2020; 727:138761. [PMID: 32330703 PMCID: PMC7166106 DOI: 10.1016/j.scitotenv.2020.138761] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 04/14/2020] [Accepted: 04/15/2020] [Indexed: 05/20/2023]
Abstract
After the cases of COVID-19 skyrocketed, showing that it was no longer possible to contain the spread of the disease, the governments of many countries launched mitigation strategies, trying to slow the spread of the epidemic and flatten its curve. The Spanish Government adopted physical distancing measures on March 14; 13 days after the epidemic outbreak started its exponential growth. Our objective in this paper was to evaluate ex-ante (before the flattening of the curve) the effectiveness of the measures adopted by the Spanish Government to mitigate the COVID-19 epidemic. Our hypothesis was that the behavior of the epidemic curve is very similar in all countries. We employed a time series design, using information from January 17 to April 5, 2020 on the new daily COVID-19 cases from Spain, China and Italy. We specified two generalized linear mixed models (GLMM) with variable response from the Gaussian family (i.e. linear mixed models): one to explain the shape of the epidemic curve of accumulated cases and the other to estimate the effect of the intervention. Just one day after implementing the measures, the variation rate of accumulated cases decreased daily, on average, by 3.059 percentage points, (95% credibility interval: -5.371, -0.879). This reduction will be greater as time passes. The reduction in the variation rate of the accumulated cases, on the last day for which we have data, has reached 5.11 percentage points. The measures taken by the Spanish Government on March 14, 2020 to mitigate the epidemic curve of COVID-19 managed to flatten the curve and although they have not (yet) managed to enter the decrease phase, they are on the way to do so.
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Affiliation(s)
- Marc Saez
- Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, Spain; CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain. http://www.udg.edu/grecs.htm
| | - Aurelio Tobias
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Spain
| | - Diego Varga
- Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, Spain; CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain; Landscape Analysis and Management Laboratory, University of Girona, Spain
| | - Maria Antònia Barceló
- Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, Spain; CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
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Adin A, Goicoa T, Ugarte MD. Online relative risks/rates estimation in spatial and spatio-temporal disease mapping. Comput Methods Programs Biomed 2019; 172:103-116. [PMID: 30846296 DOI: 10.1016/j.cmpb.2019.02.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 02/13/2019] [Accepted: 02/25/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Spatial and spatio-temporal analyses of count data are crucial in epidemiology and other fields to unveil spatial and spatio-temporal patterns of incidence and/or mortality risks. However, fitting spatial and spatio-temporal models is not easy for non-expert users. The objective of this paper is to present an interactive and user-friendly web application (named SSTCDapp) for the analysis of spatial and spatio-temporal mortality or incidence data. Although SSTCDapp is simple to use, the underlying statistical theory is well founded and all key issues such as model identifiability, model selection, and several spatial priors and hyperpriors for sensitivity analyses are properly addressed. METHODS The web application is designed to fit an extensive range of fairly complex spatio-temporal models to smooth the very often extremely variable standardized incidence/mortality risks or crude rates. The application is built with the R package shiny and relies on the well founded integrated nested Laplace approximation technique for model fitting and inference. RESULTS The use of the web application is shown through the analysis of Spanish spatio-temporal breast cancer data. Different possibilities for the analysis regarding the type of model, model selection criteria, and a range of graphical as well as numerical outputs are provided. CONCLUSIONS Unlike other software used in disease mapping, SSTCDapp facilitates the fit of complex statistical models to non-experts users without the need of installing any software in their own computers, since all the analyses and computations are made in a powerful remote server. In addition, a desktop version is also available to run the application locally in those cases in which data confidentiality is a serious issue.
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Affiliation(s)
- Aritz Adin
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain; InaMAT, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain.
| | - Tomás Goicoa
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain; InaMAT, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain.
| | - María Dolores Ugarte
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain; InaMAT, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain.
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Gutowsky LFG, Giacomini HC, de Kerckhove DT, Mackereth R, McCormick D, Chu C. Quantifying multiple pressure interactions affecting populations of a recreationally and commercially important freshwater fish. Glob Chang Biol 2019; 25:1049-1062. [PMID: 30580472 DOI: 10.1111/gcb.14556] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 12/04/2018] [Accepted: 12/14/2018] [Indexed: 06/09/2023]
Abstract
The expanding human global footprint and growing demand for freshwater have placed tremendous stress on inland aquatic ecosystems. Aichi Target 10 of the Convention on Biological Diversity aims to minimize anthropogenic pressures affecting vulnerable ecosystems, and pressure interactions are increasingly being incorporated into environmental management and climate change adaptation strategies. In this study, we explore how climate change, overfishing, forest disturbance, and invasive species pressures interact to affect inland lake walleye (Sander vitreus) populations. Walleye support subsistence, recreational, and commercial fisheries and are one of most sought-after freshwater fish species in North America. Using data from 444 lakes situated across an area of 475 000 km2 in Ontario, Canada, we apply a novel statistical tool, R-INLA, to determine how walleye biomass deficit (carrying capacity-observed biomass) is impacted by multiple pressures. Individually, angling activity and the presence of invasive zebra mussels (Dreissena polymorpha) were positively related to biomass deficits. In combination, zebra mussel presence interacted negatively and antagonistically with angling activity and percentage decrease in watershed mature forest cover. Velocity of climate change in growing degree days above 5°C and decrease in mature forest cover interacted to negatively affect walleye populations. Our study demonstrates how multiple pressure evaluations can be conducted for hundreds of populations to identify influential pressures and vulnerable ecosystems. Understanding pressure interactions is necessary to guide management and climate change adaptation strategies, and achieve global biodiversity targets.
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Affiliation(s)
- Lee F G Gutowsky
- Aquatic Research and Monitoring Section, Ontario Ministry of Natural Resources and Forestry, Peterborough, Ontario, Canada
| | - Henrique C Giacomini
- Aquatic Research and Monitoring Section, Ontario Ministry of Natural Resources and Forestry, Peterborough, Ontario, Canada
| | - Derrick T de Kerckhove
- Aquatic Research and Monitoring Section, Ontario Ministry of Natural Resources and Forestry, Peterborough, Ontario, Canada
| | - Rob Mackereth
- Centre for Northern Forest Ecosystem Research, Ontario Ministry of Natural Resources and Forestry, Thunder Bay, Ontario, Canada
| | - Darren McCormick
- Centre for Northern Forest Ecosystem Research, Ontario Ministry of Natural Resources and Forestry, Thunder Bay, Ontario, Canada
| | - Cindy Chu
- Aquatic Research and Monitoring Section, Ontario Ministry of Natural Resources and Forestry, Peterborough, Ontario, Canada
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