scholarly journals Estimating alpha, beta, and gamma diversity through deep learning

2022 ◽  
Author(s):  
Tobias Andermann ◽  
Alexandre Antonelli ◽  
Russell Barrett ◽  
Daniele Silvestro
Keyword(s):  
Species Richness ◽  
Deep Learning ◽  
Supervised Machine Learning ◽  
Individual Species ◽  
Environmental Niche ◽  
Species Lists ◽  
Alpha Beta ◽  
Ground Truthing ◽  
Environmental Niche Models ◽  
Beta And Gamma Diversity

The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truthing for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta, and gamma plant diversity at high spatial resolutions for Australia, a continent with highly contrasting diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns.

Response of dry grassland vegetation to fluctuations in weather conditions: a 9-year case study in Prague (Czech Republic)

Biologia ◽  
2011 ◽  
Vol 66 (5) ◽  
Author(s):  
Jiří Dostálek ◽  
Tomáš Frantík
Keyword(s):  
Species Richness ◽  
Czech Republic ◽  
Weather Conditions ◽  
Grass Species ◽  
Individual Species ◽  
Weather Variability ◽  
Grassland Vegetation ◽  
Dry Grassland ◽  
Local Climate ◽  
The Impact

AbstractThe extreme habitats of dry grasslands are suitable for investigations of the response of vegetation to local climate changes. The impact of weather variability on the dynamics of a plant community in a dry grassland was studied. Correlations were found between different functional groups of species and individual species and weather variability. During a 9-year study in five nature reserves in Prague (Czech Republic), the following responses of dry grassland vegetation to weather conditions were observed: (i) wetter conditions, especially in the winter, affected the dominance and species richness of perennial grass species and the decline of rosette plants; (ii) the year-to-year higher temperatures in the winter produced a decline in the dominance of short graminoids and creeping forbs; (iii) spring drought adversely impacted the overall abundance, especially the abundance of dicotyledonous species, and the species richness. However, these relationships may be manifested in different ways in different locations, and in some cases the vegetation of different locations may respond to weather conditions in opposite manners.

Diverse perennial crop mixtures sustain higher productivity over time based on ecological complementarity

2011 ◽  
Vol 26 (4) ◽  
pp. 317-327 ◽  
Author(s):  
Valentín D. Picasso ◽  
E. Charles Brummer ◽  
Matt Liebman ◽  
Philip M. Dixon ◽  
Brian J. Wilsey
Keyword(s):  
Species Richness ◽  
Species Diversity ◽  
Biomass Production ◽  
Cropping Systems ◽  
Individual Species ◽  
Selection Effects ◽  
Complementarity Effect ◽  
Change Over Time ◽  
Diversity Effects ◽  
Over Time

AbstractCropping systems that rely on renewable energy and resources and are based on ecological principles could be more stable and productive into the future than current monoculture systems with serious unintended environmental consequences such as soil erosion and water pollution. In nonagricultural systems, communities with higher species diversity have higher productivity and provide other ecosystem services. However, communities of well-adapted crop species selected for biomass production may respond differently to increasing diversity. Diversity effects may be due to complementarity among species (complementary resource use and facilitative interactions) or positive selection effects (e.g., species with higher productivity dominate the mixture), and these effects may change over time or across environments. Our goal was to identify the ecological mechanisms causing diversity effects in a biodiversity experiment using agriculturally relevant species, and evaluate the implications for the design of sustainable cropping systems. We seeded seven perennial forage species in a replicated field experiment at two locations in Iowa, USA, and evaluated biomass productivity of monocultures and two- to six-species mixtures over 3 years after the establishment year under management systems of contrasting intensity: one or three harvests per year. Productivity increased with seeded species richness in all environments, and the positive relationship did not change over time. Polyculture overyielding was due to complementarity among species in the community rather than to selection effects of individual species. Complementarity increased as a log-linear function of species richness in all environments, and this trend was consistent across years. Legume–grass facilitation may explain much of this complementarity effect. Although individual species with high biomass production had a major effect on productivity of mixtures, the species producing the highest biomass in monoculture changed over the years in most environments. Furthermore, transgressive overyielding was observed and was more prevalent in later years, in some environments. We conclude that choosing a single well-adapted species for maximizing productivity may not be the best alternative over the long term and that high levels of species diversity should be included in the design of productive and ecologically sound agricultural systems.

scholarly journals Sentiment Analysis using various Machine Learning and Deep Learning Techniques

2021 ◽  
pp. 385-394
Author(s):  
V Umarani ◽  
A Julian ◽  
J Deepa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Process ◽  
Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

scholarly journals Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data

2020 ◽  
Author(s):  
John T. Halloran ◽  
Gregor Urban ◽  
David Rocke ◽  
Pierre Baldi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Peptide Identification ◽  
Shotgun Proteomics ◽  
Database Search ◽  
Supervised Machine Learning ◽  
Superior Performance ◽  
Support Vector ◽  
Proteomics Data ◽  
Learning Classifier

AbstractSemi-supervised machine learning post-processors critically improve peptide identification of shot-gun proteomics data. Such post-processors accept the peptide-spectrum matches (PSMs) and feature vectors resulting from a database search, train a machine learning classifier, and recalibrate PSMs using the trained parameters, often yielding significantly more identified peptides across q-value thresholds. However, current state-of-the-art post-processors rely on shallow machine learning methods, such as support vector machines. In contrast, the powerful training capabilities of deep learning models have displayed superior performance to shallow models in an ever-growing number of other fields. In this work, we show that deep models significantly improve the recalibration of PSMs compared to the most accurate and widely-used post-processors, such as Percolator and PeptideProphet. Furthermore, we show that deep learning is able to adaptively analyze complex datasets and features for more accurate universal post-processing, leading to both improved Prosit analysis and markedly better recalibration of recently developed database-search functions.

scholarly journals Alpha-, beta-, and gamma-diversity of bacteria varies across habitats

PLoS ONE ◽  
2020 ◽  
Vol 15 (9) ◽  
pp. e0233872
Author(s):  
Kendra E. Walters ◽  
Jennifer B. H. Martiny
Keyword(s):  
Gamma Diversity ◽  
Alpha Beta ◽  
Beta And Gamma Diversity

scholarly journals Recent increases in assemblage rarity are linked to increasing local immigration

2020 ◽  
Vol 7 (7) ◽  
pp. 192045
Author(s):  
Faith A. M. Jones ◽  
Maria Dornelas ◽  
Anne E. Magurran
Keyword(s):  
Species Richness ◽  
Rare Species ◽  
Positive Relationship ◽  
Recent Past ◽  
Additional Species ◽  
Species Lists ◽  
Biodiversity Change

As pressures on biodiversity increase, a better understanding of how assemblages are responding is needed. Because rare species, defined here as those that have locally low abundances, make up a high proportion of assemblage species lists, understanding how the number of rare species within assemblages is changing will help elucidate patterns of recent biodiversity change. Here, we show that the number of rare species within assemblages is increasing, on average, across systems. This increase could arise in two ways: species already present in the assemblage decreasing in abundance but with no increase in extinctions, or additional species entering the assemblage in low numbers associated with an increase in immigration. The positive relationship between change in rarity and change in species richness provides evidence for the second explanation, i.e. higher net immigration than extinction among the rare species. These measurable changes in the structure of assemblages in the recent past underline the need to use multiple biodiversity metrics to understand biodiversity change.

scholarly journals Coprophagous Hydrophilid Beetles (Coleoptera, Hydrophilidae, Sphaeridiinae) Distribution in the Polish Carpathians

Insects ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 355
Author(s):  
Czesław Greń ◽  
Andrzej Górz
Keyword(s):  
Species Richness ◽  
Sea Level ◽  
Northern Europe ◽  
Individual Species ◽  
Habitat Conditions ◽  
Tatra Mountains ◽  
Baited Traps ◽  
Polish Carpathians ◽  
The Individual ◽  
The Tatra Mountains

Research on coprophagous beetles of the Hydrophilidae family in the Polish Carpathians was conducted from 2011 to 2013. The beetles were caught using baited traps. The research sites were selected to take into account both the horizontal diversity of habitat conditions and the vertical diversity associated with elevation above sea level. During the study, 9589 coprophagous hydrophilid individuals were collected, representing 17 species and five genera. Two species that were new to Poland were found: Cercyon tatricus and Pachysternum capense. The vertical ranges of the individual species of coprophagous hydrophilid beetles within the Polish Carpathians were determined as well as the elevations above sea level, with the highest and lowest species richness of this group of insects. The capture of Pachysternum capense in the Tatra Mountains may indicate the existence of an unrecognized path of migration of small insects from Southern to Northern Europe. The route and mechanisms of their migration are discussed.

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