1,046 research outputs found
Multivariate analyses in microbial ecology
Environmental microbiology is undergoing a dramatic revolution due to the increasing accumulation of biological information and contextual environmental parameters. This will not only enable a better identification of diversity patterns, but will also shed more light on the associated environmental conditions, spatial locations, and seasonal fluctuations, which could explain such patterns. Complex ecological questions may now be addressed using multivariate statistical analyses, which represent a vast potential of techniques that are still underexploited. Here, well-established exploratory and hypothesis-driven approaches are reviewed, so as to foster their addition to the microbial ecologist toolbox. Because such tools aim at reducing data set complexity, at identifying major patterns and putative causal factors, they will certainly find many applications in microbial ecology
Multivariate analyses in microbial ecology
Environmental microbiology is undergoing a dramatic revolution due to the increasing accumulation of biological information and contextual environmental parameters. This will not only enable a better identification of diversity patterns, but will also shed more light on the associated environmental conditions, spatial locations, and seasonal fluctuations, which could explain such patterns. Complex ecological questions may now be addressed using multivariate statistical analyses, which represent a vast potential of techniques that are still underexploited. Here, well-established exploratory and hypothesis-driven approaches are reviewed, so as to foster their addition to the microbial ecologist toolbox. Because such tools aim at reducing data set complexity, at identifying major patterns and putative causal factors, they will certainly find many applications in microbial ecology
ON THE USE OF UNMANNED AERIAL VEHICLES TO RAPIDLY ASSESS MICROHABITATS OF TWO TEXAS LIZARD SPECIES, COPHOSAURUS TEXANUS AND ASPIDOSCELIS GULARIS
We examined the effectiveness of using an unmanned aerial vehicle (UAV) as a tool for the rapid assessment of microhabitat in Texas spotted whiptail (Aspidoscelis gularis) and greater earless lizard (Cophosaurus texanus). We collected microhabitat data from aerial images captured at lizard sightings along gravel roadways on Devils River State Natural Area – Big Satan Unit (DRSNA-BSU) from July through September, 2014. Point locations of lizard sightings were also compared with DRSNA-BSU environmental maps including: soil type, vegetation type, Normalized Difference Vegetation Index (NDVI), elevation, and slope. Multiresponse Permutation Procedures (MRPP) and Permutational Multiple Analysis of Variance (PerMANOVA) analyses indicated that the spatial distributions of the two lizard species were significantly different. Non-metric Multidimensional Scaling (NMDS) analyses revealed that grasslands, low slopes, and soft soils were correlated with the presence of A. gularis while steep slopes, rocky soils, and the xeric plants lechuguilla, sotol, and guajillo were associated with the presence of C. texanus. Our data are consistent with other habitat association analyses administered on these two lizards. UAVs provided a new perspective on the study of microhabitat and we recommend them as a method of rapid habitat assessment. Data collection for one individual lizard in the field could be completed in less than three minutes with the use of our UAV, making the technology an ideal technique for gathering habitat data in a short amount of time
Geometry- and Accuracy-Preserving Random Forest Proximities with Applications
Many machine learning algorithms use calculated distances or similarities between data observations to make predictions, cluster similar data, visualize patterns, or generally explore the data. Most distances or similarity measures do not incorporate known data labels and are thus considered unsupervised. Supervised methods for measuring distance exist which incorporate data labels and thereby exaggerate separation between data points of different classes. This approach tends to distort the natural structure of the data. Instead of following similar approaches, we leverage a popular algorithm used for making data-driven predictions, known as random forests, to naturally incorporate data labels into similarity measures known as random forest proximities. In this dissertation, we explore previously defined random forest proximities and demonstrate their weaknesses in popular proximity-based applications. Additionally, we develop a new proximity definition that can be used to recreate the random forest’s predictions. We call these random forest-geometry-and accuracy-Preserving proximities or RF-GAP. We show by proof and empirical demonstration can be used to perfectly reconstruct the random forest’s predictions and, as a result, we argue that RF-GAP proximities provide a truer representation of the random forest’s learning when used in proximity-based applications. We provide evidence to suggest that RF-GAP proximities improve applications including imputing missing data, detecting outliers, and visualizing the data. We also introduce a new random forest proximity-based technique that can be used to generate 2- or 3-dimensional data representations which can be used as a tool to visually explore the data. We show that this method does well at portraying the relationship between data variables and the data labels. We show quantitatively and qualitatively that this method surpasses other existing methods for this task
Using Card Sorting to Explore the Mental Representation of Energy Transition Pathways Among Laypeople
Meeting international emission targets will require major changes in the energy system. This paper addresses the public perception of different pathways to energy transition, and their mental representation in particular. A study is reported that employed card sorting to explore how laypeople categorize possible pathway components with respect to their perceived similarity (Norwegian sample, n = 61; German sample, n = 71). Data sets that were obtained by this method were subjected to multidimensional scaling and cluster analysis. Results for both samples consistently indicate that people differentiate components located at the individual level (e.g., vegetarian food, avoid long flights, walking and cycling), components located at the societal level (e.g., taxes, regulations, urban planning), and components concerned with technological solutions (e.g., hydropower, wind farms, solar panels). These results give reason to assume that laypeople from Norway and Germany share a multifaceted understanding of energy transition, yet some differences between samples were present with regard to the substructure of the individual level category. Future research can build on the present results to explore the subjective meanings of these structures, possibly identifying barriers to public engagement with energy transition
Plant spectra as integrative measures of plant phenotypes
Spectroscopy at the leaf and canopy scales has attracted considerable interest in plant ecology over the past decades. Using reflectance spectra, ecologists can infer plant traits and strategies—and the community- or ecosystem-level processes they correlate with—at individual or community levels, covering more individuals and larger areas than traditional field surveys.
Because of the complex entanglement of structural and chemical factors that generate spectra, it can be tricky to understand exactly what phenotypic information they contain. We discuss common approaches to estimating plant traits from spectra—radiative transfer and empirical models—and elaborate on their strengths and limitations in terms of the causal influences of various traits on the spectrum. Many chemical traits have broad, shallow and overlapping absorption features, and we suggest that covariance among traits may have an important role in giving empirical models the flexibility to estimate such traits.
While trait estimates from reflectance spectra have been used to test ecological hypotheses over the past decades, there is also a growing body of research that uses spectra directly, without estimating specific traits. By treating positions of species in multidimensional spectral space as analogous to trait space, researchers can infer processes that structure plant communities using the information content of the full spectrum, which may be greater than any standard set of traits. We illustrate this power by showing that co-occurring grassland species are more separable in spectral space than in trait space and that the intrinsic dimensionality of spectral data is comparable to fairly comprehensive trait datasets. Nevertheless, using spectra this way may make it harder to interpret patterns in terms of specific biological processes.
Synthesis. Plant spectra integrate many aspects of plant form and function. The information in the spectrum can be distilled into estimates of specific traits, or the spectrum can be used in its own right. These two approaches may be complementary—the former being most useful when specific traits of interest are known in advance and reliable models exist to estimate them, and the latter being most useful under uncertainty about which aspects of function matter most
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An Architecture for Multilevel Learning and Robotic Control based on Concept Generation
Robot and multi-robot systems are inherently complex systems, for which designing the programs to control their behaviours proves complicated. Moreover, control programs that have been successfully designed for a particular environment and task can become useless if either of these change. It is for this reason that this thesis investigates the use of machine learning within robot and multi-robot systems. It explores an architecture for machine learning, applied to autonomous mobile robots based on dividing the learning task into two individual but interleaved sub-tasks.
The first sub-task consists of finding an appropriate representation on which to base behaviour learning. The thesis explores the viability of using multidimensional classification techniques to generalise the original sensor and motor representations into abstract hierarchies of 'concepts'. To construct concepts the research used standard classification techniques, and experimented with a novel method of multidimensional data classification based on 'Q-analysis'. Results suggest that this may be a powerful new approach to concept learning.
The second sub-task consists of using the previously acquired concepts as the representation for behaviour learning. The thesis explores whether it is possible to learn robotic behaviours represented using concepts. Results show that is possible to learn low-level behaviours such as navigation and higher-level ones such as ball passing in robot football.
The thesis concludes that the proposed architecture is viable for robotic behaviour learning and control, and that incorporating Q-analysis based classification results in a promising new approach to the control of robot and multi-robot systems
Perceptions Of Environmental Sustainability
This study examines attitudes toward environmental sustainability among college students. The new area of “sustainability reporting” identifies business practices that are associated with environmental and social costs. When these costs are known, managers can take steps to reduce them, resulting in improved profit and lessened environmental impact. Many believe it has great potential to change the way business is practiced. Responses were analyzed using multidimensional scaling analyses, permitting comparison of the perceived similarity and dissimilarity of “sustainability” to other environmentally significant terms. Results from these and semantic differential analyses showed that sustainability is perceived positively, although it is not perceived as especially dynamic nor is it associated with sound economics. Suggestions for educating students and the public about sustainability reporting are offered
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
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