Browsing by Author "Gerassis, Saki"
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- Bioclimatic modeling in the Last Glacial Maximum, Mid-Holocene and facing future climatic changes in the strawberry tree (Arbutus unedo L.)Publication . Ribeiro, M.M.A.; Roque, Natália; Ribeiro, Sílvia; Gavinhos, Catarina; Silva, Isabel Castanheira; Quinta-Nova, L.C.; Albuquerque, M.T.D.; Gerassis, SakiIncreasing forest wildfires in Portugal remain a growing concern since forests in the Mediterranean region are vulnerable to recent global warming and reduction of precipitation. Therefore, a long-term negative effect is expected on the vegetation, with increasing drought and areas burnt by fires. The strawberry tree (Arbutus unedo L.) is particularly used in Portugal to produce a spirit by processing its fruits and is the main income for forestry owners. Other applications are possible due to the fruit and leaves' anti-oxidant properties and bioactive compounds production, with a potential for clinical and food uses. It is a sclerophyllous plant, dry-adapted and fire resistant, enduring the Mediterranean climate, and recently considered as a possibility for afforestation, to intensify forest discontinuity where pines and eucalypts monoculture dominate the region. To improve our knowledge about the species' spatial distribution we used 318 plots (the centroid of a 1 km2 square grid) measuring the species presence and nine environmental attributes. The seven bioclimatic variables most impacting on the species distribution and two topographic features, slope and altitude, were used. The past, current and future climate data were obtained through WorldClim. Finally, the vulnerability of the strawberry tree to the effects of global climate change was examined in the face of two emission scenarios (RCP 4.5 and 8.5), to predict distribution changes in the years 2050 and 2070, using a species distribution models (MaxEnt). The reduction of suitable habitat for this species is significant in the southern regions, considering the future scenarios of global warming. Central and northern mountainous regions are putative predicted refuges for this species. Forest policy and management should reflect the impact of climate change on the usable areas for forestry, particularly considering species adapted to the Mediterranean regions and wildfires, such as the strawberry tree. The distribution of the species in the Last Glacial Maximum (LGM) and Mid-Holocene (MH) agrees with previous genetic and paleontological studies in the region, which support putative refuges for the species. Two in the southern and coastal-central regions, since the LGM, and one in the east-central mountainous region, considered as cryptic refugia.
- Bioclimatic modeling in the last glacial maximum, mid-holocene and facing future climatic changes in the strawberry tree (Arbutus unedo L.)Publication . Ribeiro, M.M.A.; Roque, Natália; Ribeiro, Sílvia; Gavinhos, Catarina; Silva, Isabel Castanheira; Quinta-Nova, L.C.; Albuquerque, M.T.D.; Gerassis, SakiIncreasing forest wildfires in Portugal remain a growing concern since forests in the Mediterranean region are vulnerable to recent global warming and reduction of precipitation. Therefore, a long-term negative effect is expected on the vegetation, with increasing drought and areas burnt by fires. The strawberry tree (Arbutus unedo L.) is particularly used in Portugal to produce a spirit by processing its fruits and is the main income for forestry owners. Other applications are possible due to the fruit and leaves' anti-oxidant properties and bioactive compounds production, with a potential for clinical and food uses. It is a sclerophyllous plant, dry-adapted and fire resistant, enduring the Mediterranean climate, and recently considered as a possibility for afforestation, to intensify forest discontinuity where pines and eucalypts monoculture dominate the region. To improve our knowledge about the species' spatial distribution we used 318 plots (the centroid of a 1 km2 square grid) measuring the species presence and nine environmental attributes. The seven bioclimatic variables most impacting on the species distribution and two topographic features, slope and altitude, were used. The past, current and future climate data were obtained through WorldClim. Finally, the vulnerability of the strawberry tree to the effects of global climate change was examined in the face of two emission scenarios (RCP 4.5 and 8.5), to predict distribution changes in the years 2050 and 2070, using a species distribution models (MaxEnt). The reduction of suitable habitat for this species is significant in the southern regions, considering the future scenarios of global warming. Central and northern mountainous regions are putative predicted refuges for this species. Forest policy and management should reflect the impact of climate change on the usable areas for forestry, particularly considering species adapted to the Mediterranean regions and wildfires, such as the strawberry tree. The distribution of the species in the Last Glacial Maximum (LGM) and Mid-Holocene (MH) agrees with previous genetic and paleontological studies in the region, which support putative refuges for the species. Two in the southern and coastal-central regions, since the LGM, and one in the east-central mountainous region, considered as cryptic refugia.
- Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soilsPublication . Boente, Carlos; Albuquerque, M.T.D.; Fernández-Braña, A.; Gerassis, Saki; Sierra, C.; Gallego, J.R.When considering complex scenarios involving several attributes, such as in environmental characterization, a clearer picture of reality can be achieved through the dimensional reduction of data. In this context, maps facilitate the visualization of spatial patterns of contaminant distribution and the identification of enriched areas. A set, of 15 Potentially Toxic Elements (PTEs) – (As, Ba, Cd, Co, Cr, Cu, Hg,Mo, Ni, Pb, Sb, Se, Tl, V, and Zn), was measured in soil, collected in Langreo's municipality (80 km2), Spain. Relative enrichment (RE) is introduced here to refer to the proportion of elements present in a given context. Indeed, a novel approach is provided for research into PTE fate. This method involves studying the variability of PTE proportions throughout the study area, thereby allowing the identification of dissemination trends. Traditional geostatistical approaches commonly use raw data (concentrations) accepting that the elements analyzedmake up the entirety of the soil. However, in geochemical studies the analyzed elements are just a fraction of the total soil composition. Therefore, considering compositional data is pivotal. The spatial characterization of PTEs considering raw and compositional data together allowed a broad discussion about, not only the PTEs concentration's distribution but also to reckon possible trends of relative enrichment (RE). Transformations to open closed data are widely used for this purpose. Spatial patterns have an indubitable interest. In this study, the Centered Log-ratio transformation (clr) was used, followed by its back-transformation, to build a set of compositional data that, combined with raw data, allowed to establish the sources of the PTEs and trends of spatial dissemination.
- A coupled multivariate statistics, geostatistical and machine-learning approach to address soil pollution in a prototypical Hg-mining site in a natural reservePublication . Boente, Carlos; Albuquerque, M.T.D.; Gerassis, Saki; Rodríguez-Valdés, E.; Gallego, J.R.The impact of mining activities on the environment is vast. In this regard, many mines were operating well before the introduction of environmental law. This is particularly true of cinnabar mines, whose activity has declined for decades due to growing public concern regarding Hg high toxicity. Here we present the exemplary case study of an abandoned Hg mine located in the Somiedo Natural Reserve (Spain). Until its closure in the 1970s, this mine operated under no environmental regulations, its tailings dumped in two spoil heaps, one of them located uphill and the other in the surroundings of the village of Caunedo. This study attempts to outline the degree to which soil and other environmental compartments have been affected by the two heaps. To this end, we used a novel combination of multivariate statistical, geostatistical and machine-learning methodologies. The techniques used included principal component and clustering analysis, Bayesian networks, indicator kriging, and sequential Gaussian simulations. Our results revealed high concentrations of Hg and, secondarily, As in soil but not in water or sediments. The innovative methodology abovementioned allowed us to identify natural and anthropogenic associations between 25 elements and to conclude that soil pollution was attributable mainly to natural weathering of the uphill heap. Moreover, the probability of surpassing the threshold limits and the local backgrounds was found to be high in a large extension of the area. The methodology used herein demonstrated to be effective for addressing complex pollution scenarios and therefore they are applicable to similar cases.
- A coupled multivariate statistics, geostatistical and machine-learning approach to address soil pollution in a prototypical Hg-mining site in a natural reservePublication . Boente, Carlos; Albuquerque, M.T.D.; Gerassis, Saki; Rodríguez-Valdés, E.; Gallego, J.R.The impact of mining activities on the environment is vast. In this regard, many mines were operating well before the introduction of environmental law. This is particularly true of cinnabar mines, whose activity has declined for decades due to growing public concern regarding Hg high toxicity. Here we present the exemplary case study of an abandoned Hg mine located in the Somiedo Natural Reserve (Spain). Until its closure in the 1970s, this mine operated under no environmental regulations, its tailings dumped in two spoil heaps, one of them located uphill and the other in the surroundings of the village of Caunedo. This study attempts to outline the degree to which soil and other environmental compartments have been affected by the two heaps. To this end, we used a novel combination of multivariate statistical, geostatistical and machine-learning The techniques used included principal component and clustering analysis, Bayesian networks, indicator kriging, and sequential Gaussian simulations. Our results revealed high concentrations of Hg and, secondarily, As in soil but not in water or sediments. The innovative methodology abovementioned allowed us to identify natural and anthropogenic associations between 25 elements and to conclude that soil pollution was attributable mainly to natural weathering of the uphill heap. Moreover, the probability of surpassing the threshold limits and the local backgrounds was found to 31 be high in a large extension of the area. The methodology used herein demonstrated to be effective for addressing complex pollution scenarios and therefore they are applicable to similar cases.
- Developing a new bayesian risk index for risk evaluation of soil contaminationPublication . Albuquerque, M.T.D.; Gerassis, Saki; Sierra, C.; Taboada, Javier; Martín, José; Antunes, I.M.H.R.; Gallego, J.R.Industrial and agricultural activities heavily constrain soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment alike. In this regard, the identification of areas that require remediation is crucial. In the herein research a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain). Then a stratified systematic sampling method was used at short, medium, and long distances from each zone to obtain a representative picture of the total variability of the selected attributes. The information was then combined in four risk classes (Low, Moderate, High, Remediation) following reference values from several sediment quality guidelines (SQGs). A Bayesian analysis, inferred for each zone, allowed the characterization of PTEs correlations, the unsupervised learning network technique proving to be the best fit. Based on the Bayesian network structure obtained, Pb, As and Mn were selected as key contamination parameters. For these 3 elements, the conditional probability obtained was allocated to each observed point, and a simple, direct index (Bayesian Risk Index-BRI) was constructed as a linear rating of the pre-defined risk classes weighted by the previously obtained probability. Finally, the BRI underwent geostatistical modeling. One hundred Sequential Gaussian Simulations (SGS) were computed. The Mean Image and the Standard Deviation maps were obtained, allowing the definition of High/Low risk clusters (Local G clustering) and the computation of spatial uncertainty. High-risk clusters are mainly distributed within the area with the highest altitude (agriculture/livestock) showing an associated low spatial uncertainty, clearly indicating the need for remediation. Atmospheric emissions, mainly derived from the metallurgical industry, contribute to soil contamination by PTEs.
- Future habitat suitability for species under climate change: lessons learned from the strawberry tree case studyPublication . Gerassis, Saki; Albuquerque, M.T.D; Roque, Natália; Ribeiro, Sílvia; Taboada, Javier; Ribeiro, M.M.A.Climate change is already a reality, and habitat loss is affecting relentlessly tree species distributions. The strawberry tree (Arbutus unedo L., Ericaceae) is a Mediterranean evergreen tree used in this article as a case study to evince its expected threatening situation in the upcoming age. This research work seeks to identify the bioclimatic and physiographic variables that have the most impact on the strawberry tree’s spatial distribution in Portugal, acquiring vital knowledge for the design of accurate conservation and afforestation plans for the use and conservation of strawberry tree, working as a guide under a climate change scenario. For that, twenty-one bioclimatic variables, two physiographic attributes (altitude and slope), and the Emberger Index (EI) were used together with 318 observations of strawberry trees, to build a scalable Bayesian procedure, based on machine learning techniques, aimed to assess the species’ future habitat evolution through three temporal scenarios: (i) Control Run (1960–1990); (ii) 2050 and (iii) 2070. The results indicate for 2050 a 30% loss of the humid subregion and a 35% increase in the semi-arid sub-region towards the north. In 2070, it is expected a 2% recuperation for the sub-humid area, but an 8% loss of the humid sub-area. Under these extreme climate change conditions, it is anticipated an almost complete loss of habitat for the strawberry tree in the south of Portugal. The expected ecological evolvability may trigger future migration paths and new refuges’ settlement in the northern sub-region for the succeeding decades and suggesting after 2070 the possibility of habitat switch and species drifting.
- Local versus regional soil screening levels to identify potentially polluted areasPublication . Boente, Carlos; Gerassis, Saki; Albuquerque, M.T.D.; Taboada, Javier; Gallego, J.R.Soil screening levels (SSLs) are reference threshold values required by environmental laws, established based on soil geochemical background data from often-extensive sampling areas. Such areas are often inappropriate for interpreting the true risk of pollution in small areas, since they overlook local factors (e.g., geology, industry, and traffic), which are unfeasible to encompass in large-scale samplings. To solve this issue, the calculation of local SSLs is proposed herein, performed on amajor scale closer to the area of interest. To exemplify this proposal, a soil sampling campaign was performed in the Municipality of Langreo, one of the most industrialized areas in the Principality of Asturias (northwestern Spain). Sampling allowed the measurement of local soil screening levels for several inorganic contaminants. Afterwards, a soil pollution index was calculated, referred to both regional and local thresholds, to assess the degree of contamination. Both pollution indicators were subjected to a methodology based on a Bayesian network analysis, followed by a stochastic sequential Gaussian simulation approach. The methodologies used showed differences in the identification of potentially polluted areas depending on the soil screening levels (regional or local) used. It was concluded that, in urban/industrial cores, local soil screening levels facilitate the identification of polluted areas and also reduce the uncertainty associated with sampling density and diffuse contamination. Thus, the use of local levels circumvents false-positive areas that would be classified as polluted were regional soil screening levels to be used.
- Mapping occupational health risk factors in the primary sector: a novel supervised machine learning and area-to-point poisson kriging approachPublication . Gerassis, Saki; Boente, Carlos; Albuquerque, M.T.D.; Ribeiro, M.M.A.; Abad, A.; Taboada, JavierWorkers around the world spend nearly a quarter of their time at work Occupational health is gaining great importance due to the profound impact on people long term health. The health status of the primary sector workforce is a great unknown for medical geography where health maps and spatial patterns have not been able to explain years of changing disease rates. This article proposes a new approach based on a solid characterization of the health status, which is the target node of an information theory-based Bayesian network machine-learnt from 13,000 medical examinations undertook to rural workers in Spain between 2012 and 2016. From the main health risks identified, a supervised binary logistic regression is used to produce a classification of adverse medical conditions giving rise to not healthy workers. Finally, Area-to-Point Poisson kriging is computed to provide a spatial analysis representing the incidence rate and spatial patterns of the main adverse medical conditions over the Spanish territory. The study illustrates how to overcome the challenges of working with discrete occupational data. Conceptually, high cholesterol and high glucose can be pinpointed with accuracy as the two main health risks for the working population in the primary sector.
- Modelação ecológica em medronheiro usando redes BaiesianasPublication . Albuquerque, M.T.D.; Gerassis, Saki; Roque, Natália; Ribeiro, Sílvia; Taboada, Javier; Martín, José; Ribeiro, M.M.A.O medronheiro (Arbutus unedo L.) tem potencial para ser uma cultura de sucesso comercial em várias regiões de Portugal, onde está bem-adaptado ao clima e solos. A espécie tem sido usada pelas populações locais para consumo do fruto em fresco ou processado, sobretudo como aguardente, no entanto continua a ser uma espécie largamente negligenciada, ainda que tenha muitos usos comerciais possíveis, desde a produção do fruto em fresco ou processado, a uso ornamental, farmacêutico, ou aplicações dos produtos bioativos. Além disso, devido ao seu estatuto pioneiro, é útil na recuperação dos solos, evitando a desertificação e tem, também, resistência ao fogo. A construção de um modelo ecológico para o medronho, foi obtida através de uma abordagem Baiesiana. Na investigação em curso, foi utilizada uma grelha de 1 Km2 ao longo de todo o território português (90425 parcelas), para o conjunto dos 10 atributos utilizados: sete variáveis bioclimáticas para a representação de uma "distância climática" - Bio1; Bio2; Bio5; Bio 9; Bio 15; Temperatura Máxima e Temperatura Mínima (WorldClim 1.4, 2017) e três variáveis geográficas - Altitude; Declive e Uso do Solo - para capturar uma "distância geográfica". Finalmente, a presença/ausência da espécie foi a variável objetivo. Foram usados quatro cenários: 1. Série de controle (1960-1990), visando a modelação das condições atuais; 2. Três diferentes cenários de efeito de estufa: Holocénico Médio (há 6000 anos); 2050 e 2070, utilizando o cenário de concentração de CO2 mais pessimista (RCP 8,5). As redes Baiesianas são Grafos Acíclicos Direcionados (GAD) onde os nós e os arcos tipificam as relações de causa e efeito entre variáveis em estudo. A estrutura topológica de um modelo Baiesiano reflete a dependência das variáveis e descreve a distribuição de probabilidade de certos acontecimentos, ocorrendo a condições específicas. As informações obtidas neste estudo serão utilizadas para a elaboração de regiões de proveniência, para melhoramento genético e conservação da espécie.