scholarly journals Are the vegetation structure and composition of the shrubby Caatinga free from edge influence?

2008 ◽  
Vol 22 (4) ◽  
pp. 1077-1084 ◽  
Author(s):  
André Maurício de Melo Santos ◽  
Bráulio Almeida Santos
Keyword(s):  
Vegetation Structure ◽  
Forest Ecosystems ◽  
A Priori ◽  
Principal Component ◽  
Ground Level ◽  
Stem Density ◽  
Floristic Similarity ◽  
Physical Conditions ◽  
Ecological Importance ◽  
Edge Influence

Edge influence, or edge effect, drives many biological changes in fragmented landscapes. This has been extensively studied in many forest ecosystems, but it remains to be described for the Brazilian Caatinga. Based on the biotic and physical conditions of the shrubby Caatinga, our prediction a priori was that this type of vegetation is free from edge influence in terms of vegetation structure and composition. We sampled shrubs, cacti and trees in twenty 200 m² plots on old edges (>60 yrs old) and interior of a 690 ha fragment, partially isolated and surrounded by Opuntia ficus-indica cacti. Plant height, stem diameter at ground level, stem density, species richness and diversity were statistically equal between edge and interior habitats. Magnitude of edge influence varied from -0.027 to 0.027, indicating low ecological importance of edge creation for the measured variables. Additionally, floristic similarity between habitats was 90% and a principal component analysis showed that species composition varied in a similar manner at edges and interiors. These results indicate that the vegetation of the sampled fragment is not ecologically affected by the creation of edges and suggests that changes in physical conditions and resource availability after edge creation are not enough to eliminate established plants or to alter recruitment and survival of new individuals.

scholarly journals Quantifying the Variability of Forest Ecosystem Vulnerability in the Largest Water Tower Region Globally

2021 ◽  
Vol 18 (14) ◽  
pp. 7529
Author(s):  
Siqi Sun ◽  
Yihe Lü ◽  
Da Lü ◽  
Cong Wang
Keyword(s):  
Forest Ecosystems ◽  
Human Impacts ◽  
Regional Scale ◽  
Principal Component ◽  
Tibet Plateau ◽  
Slope Aspect ◽  
Environmental Disturbances ◽  
Restoration Planning ◽  
Development Goals ◽  
Qinghai Tibet Plateau

Forests are critical ecosystems for environmental regulation and ecological security maintenance, especially at high altitudes that exhibit sensitivity to climate change and human activities. The Qinghai-Tibet Plateau—the world’s largest water tower region—has been breeding many large rivers in Asia where forests play important roles in water regulation and water quality improvement. However, the vulnerability of these forest ecosystems at the regional scale is still largely unknown. Therefore, the aim of this research is to quantitatively assess the temporal–spatial variability of forest vulnerability on the Qinghai-Tibet Plateau to illustrate the capacity of forests to withstand disturbances. Geographic information system (GIS) and the spatial principal component analysis (SPCA) were used to develop a forest vulnerable index (FVI) to assess the vulnerability of forest ecosystems. This research incorporates 15 factors covering the natural context, environmental disturbances, and socioeconomic impact. Results indicate that the measure of vulnerability was unevenly distributed spatially across the study area, and the whole trend has intensified since 2000. The three factors that contribute the most to the vulnerability of natural contexts, environmental disturbances, and human impacts are slope aspect, landslides, and the distance to the farmland, respectively. The vulnerability is higher in forest areas with lower altitudes, steeper slopes, and southerly directions. These evaluation results can be helpful for forest management in high altitude water tower regions in the forms of forest conservation or restoration planning and implementation towards sustainable development goals.

scholarly journals Regional background O<sub>3</sub> and NO<sub>x</sub> in the Houston-Galveston- Brazoria (TX) region: A decadal-scale perspective

2016 ◽  
Author(s):  
Loredana G. Suciu ◽  
Robert J. Griffin ◽  
Caroline A. Masiello
Keyword(s):  
Spatial Scales ◽  
Lower Troposphere ◽  
Principal Component ◽  
Ground Level ◽  
Ambient Air ◽  
Regional Background ◽  
Time Period ◽  
Decadal Scale ◽  
Many Sources ◽  
Local Emissions

Abstract. Ozone (O3) in the lower troposphere is harmful to people and plants, particularly during summer, when photochemistry is the most active and higher temperatures favor local chemistry. Because of its dependence on the volatile organic compounds (VOCs) to nitrogen oxides (NOx) ratio, ground-level O3 is difficult to control locally, where many sources of these precursors contribute to its mixing ratio. In addition to local emissions, chemistry and transport, larger-scale factors also contribute to local O3 and NOx. These additional contributions (often referred to as "regional background") are not well quantified within the Houston-Galveston-Brazoria (HGB) region, impeding more efficient controls on precursor emissions to achieve compliance with the National Ambient Air Quality Standards for O3. In this study, we estimate regional background O3 and NOx in the HGB region and quantify their decadal-scale trends. We use four different approaches based on principal component analysis (PCA) to quantify background O3 and NOx. Three of these approaches consist of independent PCA on both O3 and NOx for both 1-h and 8-h levels to compare our results with previous studies and to highlight the effect of both temporal and spatial scales. In the fourth approach, we co-varied O3, NOx and meteorology. Our results show that the estimation of regional background O3 has less inherent uncertainty when it was constrained by NOx and meteorology, yielding a statistically significant temporal trend of −0.69 ± 0.27 ppb y−1. Likewise, the estimation of regional background NOx trend constrained by O3 and meteorology was −0.04 ± 0.02 ppb y−1. Our best estimates of 17-y average of season-scale background O3 and NOx were 46.72 ± 2.08 ppb and 6.80 ± 0.13 ppb, respectively. Regional background O3 and NOx both have declined over time in the HGB region. This decline is likely caused by a combination of state of Texas controls on precursor emissions since 2007 and the increase in frequency of flow from the Gulf of Mexico over the same time period.

Changes in trophic abundance of soil arthropods along a grass-shrub-forest gradient

2001 ◽  
Vol 79 (3) ◽  
pp. 457-464 ◽  
Author(s):  
Steven H Ferguson
Keyword(s):  
Vegetation Structure ◽  
Ground Level ◽  
Trophic Levels ◽  
Soil Arthropods ◽  
Predacious Mites ◽  
Forest Habitat ◽  
Canadian Prairies ◽  
Productivity Gradients ◽  
Predator Abundance ◽  
Shrub Forest

Ecological theory suggests that along productivity gradients, abundances of organisms within trophic levels will increase in a stepwise pattern from producers to consumers. To test this theory I investigated changes in abundance of soil arthropods at three trophic levels: microphytophages, represented by Collembola, predacious mites (Acari) that feed on Collembola, and three groups of macroarthropods (spiders, ants, and centipedes) that were observed to feed on mites. Changes in abundance were monitored along a gradient in vegetation structure from grass to shrub to forest in the Canadian prairies. I controlled for temporal variation in abundance among years and surveys within a year. As predicted, (i) numbers of Collembola did not change with increases in productivity; (ii) mite numbers were greatest in the shrublands; and (iii) numbers of macroarthropod predators increased from grassland to shrubland, and there was a nonsignificant increase in numbers of spiders and centipedes in forest habitat. Contrary to predictions, macroarthropod numbers were not significantly greater in forest habitat, and ant numbers actually declined. Possible explanations for the lack of increase in macroarthropod predator abundance in the forest habitat with the greatest productivity include decreased ground-level humidity and greater abundance of macroarthropod predators and parasites in forest environments.

scholarly journals A Machine Learning Approach to Predict Air Quality in California

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Mauro Castelli ◽  
Fabiana Martins Clemente ◽  
Aleš Popovič ◽  
Sara Silva ◽  
Leonardo Vanneschi
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Environmental Protection Agency ◽  
Urban Areas ◽  
Principal Component ◽  
Ground Level ◽  
Support Vector ◽  
Validation Data ◽  
Pollutant Concentrations ◽  
Rbf Kernel

Predicting air quality is a complex task due to the dynamic nature, volatility, and high variability in time and space of pollutants and particulates. At the same time, being able to model, predict, and monitor air quality is becoming more and more relevant, especially in urban areas, due to the observed critical impact of air pollution on citizens’ health and the environment. In this paper, we employ a popular machine learning method, support vector regression (SVR), to forecast pollutant and particulate levels and to predict the air quality index (AQI). Among the various tested alternatives, radial basis function (RBF) was the type of kernel that allowed SVR to obtain the most accurate predictions. Using the whole set of available variables revealed a more successful strategy than selecting features using principal component analysis. The presented results demonstrate that SVR with RBF kernel allows us to accurately predict hourly pollutant concentrations, like carbon monoxide, sulfur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, as well as the hourly AQI for the state of California. Classification into six AQI categories defined by the US Environmental Protection Agency was performed with an accuracy of 94.1% on unseen validation data.

scholarly journals The influence of a priori grouping on inference of genetic clusters: simulation study and literature review of the DAPC method

Heredity ◽  
2020 ◽  
Vol 125 (5) ◽  
pp. 269-280 ◽  
Author(s):  
Joshua M. Miller ◽  
Catherine I. Cullingham ◽  
Rhiannon M. Peery
Keyword(s):  
Discriminant Analysis ◽  
Literature Review ◽  
Simulation Study ◽  
De Novo ◽  
A Priori ◽  
Principal Component ◽  
Migration Rates ◽  
Genetic Clusters ◽  
Genetic Clustering ◽  
Structure Versus

Abstract Inference of genetic clusters is a key aim of population genetics, sparking development of numerous analytical methods. Within these, there is a conceptual divide between finding de novo structure versus assessment of a priori groups. Recently developed, Discriminant Analysis of Principal Components (DAPC), combines discriminant analysis (DA) with principal component (PC) analysis. When applying DAPC, the groups used in the DA (specified a priori or described de novo) need to be carefully assessed. While DAPC has rapidly become a core technique, the sensitivity of the method to misspecification of groups and how it is being empirically applied, are unknown. To address this, we conducted a simulation study examining the influence of a priori versus de novo group designations, and a literature review of how DAPC is being applied. We found that with a priori groupings, distance between genetic clusters reflected underlying FST. However, when migration rates were high and groups were described de novo there was considerable inaccuracy, both in terms of the number of genetic clusters suggested and placement of individuals into those clusters. Nearly all (90.1%) of 224 studies surveyed used DAPC to find de novo clusters, and for the majority (62.5%) the stated goal matched the results. However, most studies (52.3%) omit key run parameters, preventing repeatability and transparency. Therefore, we present recommendations for standard reporting of parameters used in DAPC analyses. The influence of groupings in genetic clustering is not unique to DAPC, and researchers need to consider their goal and which methods will be most appropriate.

scholarly journals Optimization of Sensors to be Used in a Voltammetric Electronic Tongue Based on Clustering Metrics

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4798
Author(s):  
Munmi Sarma ◽  
Noelia Romero ◽  
Xavier Cetó ◽  
Manel del Valle
Keyword(s):  
A Priori ◽  
Electronic Tongue ◽  
Principal Component ◽  
Canonical Variate ◽  
Ann Model ◽  
Inclusion Removal ◽  
Discrimination Ability ◽  
Improved Performance ◽  
Voltammetric Electronic Tongue ◽  
The Impact

Herein we investigate the usage of principal component analysis (PCA) and canonical variate analysis (CVA), in combination with the F factor clustering metric, for the a priori tailored selection of the optimal sensor array for a given electronic tongue (ET) application. The former allows us to visually compare the performance of the different sensors, while the latter allows us to numerically assess the impact that the inclusion/removal of the different sensors has on the discrimination ability of the ET. The proposed methodology is based on the measurement of a pure stock solution of each of the compounds under study, and the posterior analysis by PCA/CVA with stepwise iterative removal of the sensors that demote the clustering when retained as part of the array. To illustrate and assess the potential of such an approach, the quantification of paracetamol, ascorbic acid, and uric acid mixtures were chosen as the study case. Initially, an array of eight different electrodes was considered, from which an optimal array of four sensors was derived to build the quantitative ANN model. Finally, the performance of the optimized ET was benchmarked against the results previously reported for the analysis of the same mixtures, showing improved performance.

scholarly journals Vegetative and Fruit Resources as Determinants of Habitat use by Mature-Forest Birds During the Postbreeding Period

The Auk ◽  
2007 ◽  
Vol 124 (2) ◽  
pp. 494-507 ◽  
Author(s):  
Andrew C. Vitz ◽  
Amanda D. Rodewald
Keyword(s):  
Vegetation Structure ◽  
A Priori ◽  
Forest Birds ◽  
Canopy Height ◽  
Theoretic Approach ◽  
Mature Forest ◽  
Risk Of Predation ◽  
Structure Density ◽  
Information Theoretic Approach ◽  
Capture Rates

AbstractRecent studies have demonstrated that many birds of mature forests heavily use early-successional habitat during the postbreeding period. Two frequently invoked hypotheses to explain these shifts are that postbreeding birds select (1) dense cover to reduce risk of predation and (2) abundant fruit resources to facilitate foraging. Using mist nets between 15 June and 16 August in 2002 and 2003, we captured mature-forest birds during the postbreeding period in 12 regenerating hardwood clearcuts (three to seven years old) in southeast Ohio. Vegetation structure and fruit resources were measured at nine net locations within each clearcut. We applied an information-theoretic approach, where we used vegetative and fruit variables as predictors in seven a priori models to evaluate how habitat factors might explain capture rates. In two seasons, we captured 1,089 hatch-year (HY) and 445 after-hatch-year (AHY) postbreeding birds of 32 mature-forest species. In general, models reflecting habitat structure (density of low vegetation, canopy height) best explained variation in capture rates, which were negatively related to the density of low vegetation (<1.5 m) and positively related to canopy height. Extremely dense low vegetation may be disadvantageous if it inhibits movements and provides cryptic locations for ground predators. Instead, habitats with greater vertical structure (e.g., taller vegetation) and relatively less dense low vegetation below may provide better protection from aerial and ground predators. Overall, results suggest that vegetation structure may explain high use of early-successional forests by many birds during the postbreeding period, though fruit may be the most important factor for seasonal frugivores.Recursos Vegetativos y de Frutos como Determinantes del Uso de Hábitat por Aves de Bosque Maduro Durante el Período Posterior a la Reproducción

Novel introductions and epidemic dynamics of the sudden oak death pathogen Phytophthora ramorum in Oregon forests

2020 ◽  
Author(s):  
Nicholas C Carleson ◽  
Hazel Daniels ◽  
Paul Reeser ◽  
Alan Kanaskie ◽  
Sarah Navarro ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Population Dynamics ◽  
Microsatellite Loci ◽  
Forest Ecosystems ◽  
Principal Component ◽  
Phytophthora Ramorum ◽  
Sudden Oak Death ◽  
Clonal Lineage ◽  
Epidemic Dynamics ◽  
One Year

Sudden oak death caused by Phytophthora ramorum has been actively managed in Oregon since the early 2000’s. To date, this epidemic has been driven mostly by the NA1 clonal lineage of P. ramorum, but an outbreak of the EU1 lineage has recently emerged. Here we contrast the population dynamics of the NA1 outbreak first reported in 2001 to the outbreak of the EU1 lineage first detected in 2015. We tested if any of the lineages were introduced more than once. Infested regions of the forest were sampled between 2013-2018 (n = 903) and strains were genotyped at 15 microsatellite loci. Most genotypes observed were transient, with 272 of 358 unique genotypes emerging one year and disappearing the next. Diversity of EU1 was very low and isolates were spatially clustered (< 8 km apart), suggesting a single EU1 introduction. Some forest isolates are genetically similar to isolates collected from a local nursery in 2012, suggesting introduction of EU1 from this nursery or simultaneous introduction to both the nursery and latently into the forest. In contrast, the older NA1 populations were more polymorphic and spread over 30 km2. Principal component analysis supported two to four independent NA1 introductions. The NA1 and EU1 epidemics infest the same area but show disparate demographics owing to initial introductions of the lineages spaced 10 years apart. Comparing these epidemics provides novel insights into patterns of emergence of clonal pathogens in forest ecosystems.

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