Conservation priorities and distribution patterns of vascular plant species along environmental gradients in Aberdare ranges forest

Distribution patterns of biodiversity and the factors influencing them are important in conservation and management strategies of natural resources. With impending threats from increased human population and global climatic changes, there is an urgent need for a comprehensive understanding of these patterns, more so in species-rich tropical montane ecosystems where little is known about plant diversity and distribution. Vascular species richness along elevation and climatic gradients of Aberdare ranges forest were explored. A total of 1337 species in 137 families, 606 genera, 82 subspecies and 80 varieties were recorded. Correlations, simple linear regression and Partial least square regression analysis were used to assess richness and diversity patterns of total plants, herbs, shrubs, climbers, arboreal and endemic species from 2000–4000 m above sea level. Total plant species richness showed a monotonic declining relationship with elevation with richness maxima at 2000–2100 m a.s.l., while endemic species richness had a positive unimodal increase along elevation with peaks at 3600–3700 m a.s.l. Herbs, shrubs, climbers and arboreal had significant negative relationships with altitude, excluding endemism which showed positive relations. In contrast, both air and soil temperatures had positive relationships with taxa richness groups and negative relations with endemic species. Elevation was found to have higher relative influence on plant richness and distribution in Aberdare ranges forest. For effective conservation and management of biodiversity in Aberdare, localized dynamic conservation interventions are recommended in contrast to broad and static strategies. Establishment of conservation zones and migration corridors are necessary to safeguard biodiversity in line with envisaged global climatic vicissitudes.

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Potential of LiDAR for species richness prediction at Mount Kilimanjaro

10.5194/egusphere-egu2020-5174 ◽

2020 ◽

Author(s):

Alice Ziegler ◽

Keyword(s):

Species Richness ◽

Structural Information ◽

Elevation Gradient ◽

Partial Least Square ◽

Airborne Lidar ◽

Least Square ◽

Trophic Levels ◽

Partial Least Square Regression ◽

Mount Kilimanjaro ◽

The Impact

To mitigate the negative effects of biodiversity loss, monitoring of species and functional diversity is an important prerequisite for focused management plans. However, sampling of biodiversity during field campaigns is labor- and cost-intensive. Therefore, researchers often use proxies extracted from three-dimensional and high-resolution airborne LiDAR (Light Detection and Ranging) data of the vegetation for predicting biodiversity measures (e.g. species richness or diversity).This study aims at (i) assessing the suitability of LiDAR observations to map species richness across 17 taxonomic groups and four trophic levels at Mount Kilimanjaro and (ii) differentiating the predictive power of LiDAR-derived structural information from what is already explained by elevation, thereby comparing the prediction potential across taxa and trophic levels.The field data for this study were collected across 59 plots along an elevation gradient of about 4000 meters at the southern slopes of Mount Kilimanjaro using established methods to sample the selected groups of organisms. The prediction is accomplished with three consecutive steps: (1) Species richness of each taxon is estimated using Partial Least Square Regression (PLSR) with only elevation and its square as independent variables. (2) The residuals of this model are then predicted using the LiDAR-derived variables and PLSR. (3) This third model is subsequently compared to a model that uses the same LiDAR-derived variables and PLSR to predict species richness directly rather than its residuals. This procedure allows to analyze the impact of elevation versus structure on each taxon. Furthermore, the standardized study design allows to compare the predictability of species richness across the selected groups of organisms.Results of this study show that most taxa can be best predicted by elevation, even though in most cases the structural models perform almost equally. As expected, results of the model performances of trophic levels indicate, that herbivores are influenced more by structure than decomposers and generalists.

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Vascular plant species diversity of Mt. Etna (Sicily): endemicity, insularity and spatial patterns along the altitudinal gradient of the highest active volcano in Europe

PeerJ ◽

10.7717/peerj.9875 ◽

2020 ◽

Vol 8 ◽

pp. e9875

Author(s):

Saverio Sciandrello ◽

Pietro Minissale ◽

Gianpietro Giusso del Galdo

Keyword(s):

Species Richness ◽

Plant Species ◽

Endemic Species ◽

Altitudinal Gradient ◽

Hot Spot ◽

Vascular Plant ◽

Plant Conservation ◽

Active Volcano ◽

Plant Species Richness ◽

Mt Etna

Background Altitudinal variation in vascular plant richness and endemism is crucial for the conservation of biodiversity. Territories featured by a high species richness may have a low number of endemic species, but not necessarily in a coherent pattern. The main aim of our research is to perform an in-depth survey on the distribution patterns of vascular plant species richness and endemism along the elevation gradient of Mt. Etna, the highest active volcano in Europe. Methods We used all the available data (literature, herbarium and seed collections), plus hundreds of original (G Giusso, P Minissale, S Sciandrello, pers. obs., 2010–2020) on the occurrence of the Etna plant species. Mt. Etna (highest peak at 3,328 mt a.s.l.) was divided into 33 belts 100 m wide and the species richness of each altitudinal range was calculated as the total number of species per interval. In order to identify areas with high plant conservation priority, 29 narrow endemic species (EE) were investigated through hot spot analysis using the “Optimized Hot Spot Analysis” tool available in the ESRI ArcGIS software package. Results Overall against a floristic richness of about 1,055 taxa, 92 taxa are endemic, of which 29 taxa are exclusive (EE) of Mt. Etna, 27 endemic of Sicily (ES) and 35 taxa endemic of Italy (EI). Plant species richness slowly grows up to 1,000 m, then decreases with increasing altitude, while endemic richness shows an increasing percentage incidence along the altitudinal gradient (attributed to the increased isolation of higher elevation). The highest endemic richness is recorded from 2,000 up to 2,800 m a.s.l., while the highest narrow endemic richness (EE) ranges from 2,500 up to 2,800 m a.s.l. Life-form patterns clearly change along altitudinal gradient. In regard to the life-form of the endemics, the most represented are the hemicryptophytes, annual plants (therophytes) are prevailing at lower altitudes and show a decreasing trend with increasing elevation, while chamaephytes are featured by an increasing trend up to 3,100 m of altitude. Furthermore, the results of the hotspot analysis emphasize the high plant conservation priority areas localized in oro-mediterranean (1,800–2,400 m s.l.m.) and cryo-mediterranean (2,400–2,800 m) bioclimatic belts, in correspondence of the oldest substrates of the volcano. Conclusions High plant speciation rate caused by increasing isolation with elevation is the most plausible explanation for the largest active volcano in Europe. The high degree of endemic species on Mt. Etna is linked to its geographical, geological and climatic isolation, all important drivers of speciation acting on the population gene flows. The hot spot map obtained represents a useful support for help environmental decision makers to identify priority areas for plant conservation.

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Patterns of plant diversity and endemism in Namibia

Bothalia ◽

10.4102/abc.v36i2.360 ◽

2006 ◽

Vol 36 (2) ◽

pp. 175-189 ◽

Cited By ~ 2

Author(s):

P. Craven ◽

P. Vorster

Keyword(s):

Species Richness ◽

Plant Species ◽

Endemic Species ◽

Distribution Patterns ◽

The Other ◽

Species Overlap ◽

High Species Diversity ◽

Total Plant ◽

Endemic Flora ◽

Species Being

Species richness, endemism and areas that are rich in both species and endemic species were assessed and mapped for Namibia. High species diversity corresponds with zones where species overlap. These are particularly obvious where there are altitudinal variations and in high-lying areas. The endemic flora of Namibia is rich and diverse. An estimated 16% of the total plant species in Namibia are endemic to the country. Endemics are in a wide variety of families and sixteen genera are endemic. Factors that increase the likelihood of endemism are mountains, hot deserts, diversity of substrates and microclimates. The distribution of plants endemic to Namibia was arranged in three different ways. Firstly, based on a grid count with the phytogeographic value o f the species being equal, overall endemism was mapped. Secondly, range restricted plant species were mapped individually and those with congruent distribution patterns were combined. Thirdly, localities that are important for very range-restricted species were identified. The resulting maps o f endemism and diversity were compared and found to correspond in many localities. When overall endemism is compared with overall diversity, rich localities may consist of endemic species with wide ranges. The other methods identify important localities with their own distinctive complement of species.

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Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Remote Sensing ◽

10.3390/rs13040641 ◽

2021 ◽

Vol 13 (4) ◽

pp. 641

Author(s):

Gopal Ramdas Mahajan ◽

Bappa Das ◽

Dayesh Murgaokar ◽

Ittai Herrmann ◽

Katja Berger ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Spectral Indices ◽

Learning Models ◽

Leaf Nutrients ◽

Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

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Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

Applied Sciences ◽

10.3390/app11020618 ◽

2021 ◽

Vol 11 (2) ◽

pp. 618

Author(s):

Tanvir Tazul Islam ◽

Md Sajid Ahmed ◽

Md Hassanuzzaman ◽

Syed Athar Bin Amir ◽

Tanzilur Rahman

Keyword(s):

Blood Glucose ◽

Blood Glucose Level ◽

Glucose Level ◽

Quantitative Estimation ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Frequency Noise ◽

Optical Noise

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

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A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽

10.3390/molecules26061546 ◽

2021 ◽

Vol 26 (6) ◽

pp. 1546

Author(s):

Ioanna Dagla ◽

Anthony Tsarbopoulos ◽

Evagelos Gikas

Keyword(s):

Design Of Experiments ◽

High Performance ◽

Similarity Index ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Distance Measures ◽

Linear Regression Models ◽

Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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The inflammatory profile of cerebrospinal fluid, plasma, and saliva from patients with severe neuropathic pain and healthy controls-a pilot study

BMC Neuroscience ◽

10.1186/s12868-021-00608-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Mika Jönsson ◽

Björn Gerdle ◽

Bijar Ghafouri ◽

Emmanuel Bäckryd

Keyword(s):

Cerebrospinal Fluid ◽

Neuropathic Pain ◽

Pain Intensity ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Therapeutic Interventions ◽

Healthy Controls ◽

Inflammatory Profile

Abstract Background Neuropathic pain (NeuP) is a complex, debilitating condition of the somatosensory system, where dysregulation between pro- and anti-inflammatory cytokines and chemokines are believed to play a pivotal role. As of date, there is no ubiquitously accepted diagnostic test for NeuP and current therapeutic interventions are lacking in efficacy. The aim of this study was to investigate the ability of three biofluids - saliva, plasma, and cerebrospinal fluid (CSF), to discriminate an inflammatory profile at a central, systemic, and peripheral level in NeuP patients compared to healthy controls. Methods The concentrations of 71 cytokines, chemokines and growth factors in saliva, plasma, and CSF samples from 13 patients with peripheral NeuP and 13 healthy controls were analyzed using a multiplex-immunoassay based on an electrochemiluminescent detection method. The NeuP patients were recruited from a clinical trial of intrathecal bolus injection of ziconotide (ClinicalTrials.gov identifier NCT01373983). Multivariate data analysis (principal component analysis and orthogonal partial least square regression) was used to identify proteins significant for group discrimination and protein correlation to pain intensity. Proteins with variable influence of projection (VIP) value higher than 1 (combined with the jack-knifed confidence intervals in the coefficients plot not including zero) were considered significant. Results We found 17 cytokines/chemokines that were significantly up- or down-regulated in NeuP patients compared to healthy controls. Of these 17 proteins, 8 were from saliva, 7 from plasma, and 2 from CSF samples. The correlation analysis showed that the most important proteins that correlated to pain intensity were found in plasma (VIP > 1). Conclusions Investigation of the inflammatory profile of NeuP showed that most of the significant proteins for group separation were found in the less invasive biofluids of saliva and plasma. Within the NeuP patient group it was also seen that proteins in plasma had the highest correlation to pain intensity. These preliminary results indicate a potential for further biomarker research in the more easily accessible biofluids of saliva and plasma for chronic peripheral neuropathic pain where a combination of YKL-40 and MIP-1α in saliva might be of special interest for future studies that also include other non-neuropathic pain states.

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Suggestions on the Contribution of Methyl Eugenol and Eugenol to Bay Laurel (Laurus nobilis L.) Essential Oil Preservative Activity through Radical Scavenging

Molecules ◽

10.3390/molecules26082342 ◽

2021 ◽

Vol 26 (8) ◽

pp. 2342

Author(s):

Nikolaos Nenadis ◽

Maria Papapostolou ◽

Maria Z. Tsimidou

Keyword(s):

Essential Oil ◽

Radical Scavenging Activity ◽

Pearson Correlation ◽

Radical Scavenging ◽

Methyl Eugenol ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Positive Contribution ◽

Scavenging Activity

The present study examined the radical scavenging potential of the two benzene derivatives found in the bay laurel essential oil (EO), namely methyl eugenol (MEug) and eugenol (Eug), theoretically and experimentally to make suggestions on their contribution to the EO preservative activity through such a mechanism. Calculation of appropriate molecular indices widely used to characterize chain-breaking antioxidants was carried out in the gas and liquid phases (n-hexane, n-octanol, methanol, water). Experimental evidence was based on the DPPH• scavenging assay applied to pure compounds and a set of bay laurel EOs chemically characterized with GC-MS/FID. Theoretical calculations suggested that the preservative properties of both compounds could be exerted through a radical scavenging mechanism via hydrogen atom donation. Eug was predicted to be of superior efficiency in line with experimental findings. Pearson correlation and partial least square regression analyses of the EO antioxidant activity values vs. % composition of individual volatiles indicated the positive contribution of both compounds to the radical scavenging activity of bay laurel EOs. Eug, despite its low content in bay laurel EOs, was found to influence the most the radical scavenging activity of the latter.

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