Integrated environmental monitoring and multivariate data analysis-A case study

Eide, Ingvar; Westad, Frank; Nilssen, Ingunn; Freitas, Felipe Sales de; Santos, Natália Gomes dos; Santos, Francisco dos; Cabral, Marcelo Montenegro; Bı́cego, Márcia Caruso; Figueira, Rubens César Lopes; Johnsen, Ståle

doi:10.1002/ieam.1840

Cited by 5 publications

(4 citation statements)

References 39 publications

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“…The first four PCA axes explained a large proportion (64%) of the variation in environmental variables across forest types and sites (Table ), compared to others using PCA and environmental data (e.g., Eide et al. , Lévesque et al. ).…”

Section: Resultsmentioning

confidence: 82%

Rhododendron maximumimpacts seed bank composition and richness followingTsuga canadensisloss in riparian forests

et al. 2018

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Miniat. 2018. Rhododendron maximum impacts seed bank composition and richness following Tsuga canadensis loss in riparian forests. Ecosphere 9(4):e02204. 10. 1002/ecs2.2204 Abstract. Southern Appalachian riparian forests have undergone changes in composition and function from invasive pathogens and pests. Castanea dentata mortality in the 1930s from chestnut blight (Cryphonectria parasitica) and Tsuga canadensis mortality in the 2000s from the hemlock woolly adelgid (Adelges tsugae) have led to the expansion and increased growth of Rhododendron maximum, an evergreen subcanopy shrub. A better understanding of seed bank characteristics and the various abiotic and biotic factors that affect the seed bank may be useful in determining the restoration potential of forest communities following invasion-related disturbances. We compared the seed bank of two deciduous forest types: hardwood forests with a dense R. maximum subcanopy (hereafter, RR) and hardwood forests without R. maximum (hereafter, HWD). We evaluated numerous microenvironmental variables through principal component analysis (PCA) and correlated the derived PCA axes scores to seed bank density and richness across forest types. We found that seed bank density was comparable between the forests types; however, seed bank richness was much lower in RR than HWD and the species composition was dissimilar between forest types. Twenty-eight of 64 (44%) species in the seed bank of HWD were not found in the seed bank of RR. Species that were represented in both forest types were often found in contrasting densities. Most notably, seed bank densities of several woody species were considerably higher in RR (85%) than HWD (45%), while herbaceous seed bank density was lower in RR (11%) than HWD (50%). Mineral soil pH, soil nutrient availability, and soil moisture were lower, and organic soil (Oi + Oe + Oa) depth and mass were greater in the RR than HWD forest type. PCA correlations revealed that PCA4 (represented by understory density and Oe + Oa phosphorus and carbon/nitrogen ratio) was negatively correlated with total seed bank density. PCA1 (represented by Oe + Oa cations and phosphorus, understory richness, ground-layer cover, and mineral soil pH) and PCA4 were positively correlated with total seed bank richness. These results suggest that the soil seed bank will not be the primary mode of recruitment to establish a diverse and herbaceousrich community if a RR is present.

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Section: Resultsmentioning

confidence: 82%

Rhododendron maximumimpacts seed bank composition and richness followingTsuga canadensisloss in riparian forests

et al. 2018

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“…Hyperspectral imaging generates large amounts of data which require sophisticated data analysis and machine learning methods [19]. Multivariate data analysis and machine learning have been used successfully in several marine environmental studies for the interpretation of large data sets, for example in integrated environmental monitoring [20] and for the analyses of photos to assess the potential impact of water-based drill cuttings on deep-water rhodolith-forming calcareous algae [21].…”

Section: Introductionmentioning

confidence: 99%

Underwater hyperspectral classification of deep sea corals exposed to 2-methylnaphthalene

et al. 2019

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Coral reefs around the world are under threat due to anthropogenic impacts on the environment. It is therefore important to develop methods to monitor the status of the reefs and detect changes in the health condition of the corals at an early stage before severe damage occur. In this work, we evaluate underwater hyperspectral imaging as a method to detect changes in health status of both orange and white color morphs of the coral species Lophelia pertusa . Differing health status was achieved by exposing 60 coral samples to the toxic compound 2-methylnaphthalene in concentrations of 0 mg L −1 to 3.5 mg L −1 . A machine learning model was utilized to classify corals according to lethal concentration (LC) levels LC5 (5% mortality) and LC25 (25% mortality), solely based on their reflectance spectra. All coral samples were classified to correct concentration group. This is a first step towards developing a remote sensing technique able to assess environmental impact on deep-water coral habitats over larger areas.

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“…Multivariate data analysis has successfully been used in several marine environmental studies for the interpretation of large data sets, for example on heavy metals and biomarker response [ 6 ], on spatial and temporal distribution of heavy metals in seawater and sediments [ 7 ], on element release from sediments [ 8 ], on Polycyclic Aromatic Hydrocarbons (PAH) in marine and freshwater sediments [ 9 – 10 ], and to predict toxicity from sediment chemistry [ 11 ]. In a recent study, multivariate data analysis was used in integrated environmental monitoring, comprising not only chemical, physical and biological variables but also discharge data at the Peregrino oil field off the coast of Brazil [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring

Eide

Westad

2018

PLoS ONE

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A pilot study demonstrating real-time environmental monitoring with automated multivariate analysis of multi-sensor data submitted online has been performed at the cabled LoVe Ocean Observatory located at 258 m depth 20 km off the coast of Lofoten-Vesterålen, Norway. The major purpose was efficient monitoring of many variables simultaneously and early detection of changes and time-trends in the overall response pattern before changes were evident in individual variables. The pilot study was performed with 12 sensors from May 16 to August 31, 2015. The sensors provided data for chlorophyll, turbidity, conductivity, temperature (three sensors), salinity (calculated from temperature and conductivity), biomass at three different depth intervals (5–50, 50–120, 120–250 m), and current speed measured in two directions (east and north) using two sensors covering different depths with overlap. A total of 88 variables were monitored, 78 from the two current speed sensors. The time-resolution varied, thus the data had to be aligned to a common time resolution. After alignment, the data were interpreted using principal component analysis (PCA). Initially, a calibration model was established using data from May 16 to July 31. The data on current speed from two sensors were subject to two separate PCA models and the score vectors from these two models were combined with the other 10 variables in a multi-block PCA model. The observations from August were projected on the calibration model consecutively one at a time and the result was visualized in a score plot. Automated PCA of multi-sensor data submitted online is illustrated with an attached time-lapse video covering the relative short time period used in the pilot study. Methods for statistical validation, and warning and alarm limits are described. Redundant sensors enable sensor diagnostics and quality assurance. In a future perspective, the concept may be used in integrated environmental monitoring.

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Integrated environmental monitoring and multivariate data analysis-A case study

Cited by 5 publications

References 39 publications

Rhododendron maximumimpacts seed bank composition and richness followingTsuga canadensisloss in riparian forests

Rhododendron maximumimpacts seed bank composition and richness followingTsuga canadensisloss in riparian forests

Underwater hyperspectral classification of deep sea corals exposed to 2-methylnaphthalene

Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring

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