20,137 research outputs found
Ecological models at fish community and species level to support effective river restoration
RESUMEN
Los peces nativos son indicadores de la salud de los ecosistemas acuáticos, y se han
convertido en un elemento de calidad clave para evaluar el estado ecológico de los ríos. La
comprensión de los factores que afectan a las especies nativas de peces es importante para la
gestión y conservación de los ecosistemas acuáticos. El objetivo general de esta tesis es analizar
las relaciones entre variables biológicas y de hábitat (incluyendo la conectividad) a través de
una variedad de escalas espaciales en los ríos Mediterráneos, con el desarrollo de herramientas
de modelación para apoyar la toma de decisiones en la restauración de ríos.
Esta tesis se compone de cuatro artículos. El primero tiene como objetivos modelar la
relación entre un conjunto de variables ambientales y la riqueza de especies nativas (NFSR), y
evaluar la eficacia de potenciales acciones de restauración para mejorar la NFSR en la cuenca
del río Júcar. Para ello se aplicó un enfoque de modelación de red neuronal artificial (ANN),
utilizando en la fase de entrenamiento el algoritmo Levenberg-Marquardt. Se aplicó el método
de las derivadas parciales para determinar la importancia relativa de las variables ambientales.
Según los resultados, el modelo de ANN combina variables que describen la calidad de ribera,
la calidad del agua y el hábitat físico, y ayudó a identificar los principales factores que
condicionan el patrón de distribución de la NFSR en los ríos Mediterráneos. En la segunda parte
del estudio, el modelo fue utilizado para evaluar la eficacia de dos acciones de restauración en el
río Júcar: la eliminación de dos azudes abandonados, con el consiguiente incremento de la
proporción de corrientes. Estas simulaciones indican que la riqueza aumenta con el incremento
de la longitud libre de barreras artificiales y la proporción del mesohabitat de corriente, y
demostró la utilidad de las ANN como una poderosa herramienta para apoyar la toma de
decisiones en el manejo y restauración ecológica de los ríos Mediterráneos.
El segundo artículo tiene como objetivo determinar la importancia relativa de los dos
principales factores que controlan la reducción de la riqueza de peces (NFSR), es decir, las
interacciones entre las especies acuáticas, variables del hábitat (incluyendo la conectividad
fluvial) y biológicas (incluidas las especies invasoras) en los ríos Júcar, Cabriel y Turia. Con
este fin, tres modelos de ANN fueron analizados: el primero fue construido solamente con
variables biológicas, el segundo se construyó únicamente con variables de hábitat y el tercero
con la combinación de estos dos grupos de variables. Los resultados muestran que las variables
de hábitat son los ¿drivers¿ más importantes para la distribución de NFSR, y demuestran la
importancia ecológica de los modelos desarrollados. Los resultados de este estudio destacan la
necesidad de proponer medidas de mitigación relacionadas con la mejora del hábitat
(incluyendo la variabilidad de caudales en el río) como medida para conservar y restaurar los
ríos Mediterráneos.
El tercer artículo busca comparar la fiabilidad y relevancia ecológica de dos modelos
predictivos de NFSR, basados en redes neuronales artificiales (ANN) y random forests (RF). La
relevancia de las variables seleccionadas por cada modelo se evaluó a partir del conocimiento
ecológico y apoyado por otras investigaciones. Los dos modelos fueron desarrollados utilizando
validación cruzada k-fold y su desempeño fue evaluado a través de tres índices: el coeficiente de determinación (R2
), el error cuadrático medio (MSE) y el coeficiente de determinación ajustado
(R2
adj). Según los resultados, RF obtuvo el mejor desempeño en entrenamiento. Pero, el
procedimiento de validación cruzada reveló que ambas técnicas generaron resultados similares
(R2
= 68% para RF y R2
= 66% para ANN). La comparación de diferentes métodos de machine
learning es muy útil para el análisis crítico de los resultados obtenidos a través de los modelos.
El cuarto artículo tiene como objetivo evaluar la capacidad de las ANN para identificar los
factores que afectan a la densidad y la presencia/ausencia de Luciobarbus guiraonis en la
demarcación hidrográfica del Júcar. Se utilizó una red neuronal artificial multicapa de tipo feedforward (ANN) para representar relaciones no lineales entre descriptores de L. guiraonis con
variables biológicas y de hábitat. El poder predictivo de los modelos se evaluó con base en el
índice Kappa (k), la proporción de casos correctamente clasificados (CCI) y el área bajo la curva
(AUC) característica operativa del receptor (ROC). La presencia/ausencia de L. guiraonis fue
bien predicha por el modelo ANN (CCI = 87%, AUC = 0.85 y k = 0.66). La predicción de la
densidad fue moderada (CCI = 62%, AUC = 0.71 y k = 0.43). Las variables más importantes
que describen la presencia/ausencia fueron: radiación solar, área de drenaje y la proporción de
especies exóticas de peces con un peso relativo del 27.8%, 24.53% y 13.60% respectivamente.
En el modelo de densidad, las variables más importantes fueron el coeficiente de variación de
los caudales medios anuales con una importancia relativa del 50.5% y la proporción de especies
exóticas de peces con el 24.4%. Los modelos proporcionan información importante acerca de la
relación de L. guiraonis con variables bióticas y de hábitat, este nuevo conocimiento podría
utilizarse para apoyar futuros estudios y para contribuir en la toma de decisiones para la
conservación y manejo de especies en los en los ríos Júcar, Cabriel y Turia.Olaya Marín, EJ. (2013). Ecological models at fish community and species level to support effective river restoration [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/28853TESI
Cosmological Constraints from Galaxy Clustering and the Mass-to-Number Ratio of Galaxy Clusters
We place constraints on the average density (Omega_m) and clustering
amplitude (sigma_8) of matter using a combination of two measurements from the
Sloan Digital Sky Survey: the galaxy two-point correlation function, w_p, and
the mass-to-galaxy-number ratio within galaxy clusters, M/N, analogous to
cluster M/L ratios. Our w_p measurements are obtained from DR7 while the sample
of clusters is the maxBCG sample, with cluster masses derived from weak
gravitational lensing. We construct non-linear galaxy bias models using the
Halo Occupation Distribution (HOD) to fit both w_p and M/N for different
cosmological parameters. HOD models that match the same two-point clustering
predict different numbers of galaxies in massive halos when Omega_m or sigma_8
is varied, thereby breaking the degeneracy between cosmology and bias. We
demonstrate that this technique yields constraints that are consistent and
competitive with current results from cluster abundance studies, even though
this technique does not use abundance information. Using w_p and M/N alone, we
find Omega_m^0.5*sigma_8=0.465+/-0.026, with individual constraints of
Omega_m=0.29+/-0.03 and sigma_8=0.85+/-0.06. Combined with current CMB data,
these constraints are Omega_m=0.290+/-0.016 and sigma_8=0.826+/-0.020. All
errors are 1-sigma. The systematic uncertainties that the M/N technique are
most sensitive to are the amplitude of the bias function of dark matter halos
and the possibility of redshift evolution between the SDSS Main sample and the
maxBCG sample. Our derived constraints are insensitive to the current level of
uncertainties in the halo mass function and in the mass-richness relation of
clusters and its scatter, making the M/N technique complementary to cluster
abundances as a method for constraining cosmology with future galaxy surveys.Comment: 23 pages, submitted to Ap
Cosmological Parameters from Observations of Galaxy Clusters
Studies of galaxy clusters have proved crucial in helping to establish the
standard model of cosmology, with a universe dominated by dark matter and dark
energy. A theoretical basis that describes clusters as massive,
multi-component, quasi-equilibrium systems is growing in its capability to
interpret multi-wavelength observations of expanding scope and sensitivity. We
review current cosmological results, including contributions to fundamental
physics, obtained from observations of galaxy clusters. These results are
consistent with and complementary to those from other methods. We highlight
several areas of opportunity for the next few years, and emphasize the need for
accurate modeling of survey selection and sources of systematic error.
Capitalizing on these opportunities will require a multi-wavelength approach
and the application of rigorous statistical frameworks, utilizing the combined
strengths of observers, simulators and theorists.Comment: 53 pages, 21 figures. To appear in Annual Review of Astronomy &
Astrophysic
Quantitative Perspectives on Fifty Years of the Journal of the History of Biology
Journal of the History of Biology provides a fifty-year long record for
examining the evolution of the history of biology as a scholarly discipline. In
this paper, we present a new dataset and preliminary quantitative analysis of
the thematic content of JHB from the perspectives of geography, organisms, and
thematic fields. The geographic diversity of authors whose work appears in JHB
has increased steadily since 1968, but the geographic coverage of the content
of JHB articles remains strongly lopsided toward the United States, United
Kingdom, and western Europe and has diversified much less dramatically over
time. The taxonomic diversity of organisms discussed in JHB increased steadily
between 1968 and the late 1990s but declined in later years, mirroring broader
patterns of diversification previously reported in the biomedical research
literature. Finally, we used a combination of topic modeling and nonlinear
dimensionality reduction techniques to develop a model of multi-article fields
within JHB. We found evidence for directional changes in the representation of
fields on multiple scales. The diversity of JHB with regard to the
representation of thematic fields has increased overall, with most of that
diversification occurring in recent years. Drawing on the dataset generated in
the course of this analysis, as well as web services in the emerging digital
history and philosophy of science ecosystem, we have developed an interactive
web platform for exploring the content of JHB, and we provide a brief overview
of the platform in this article. As a whole, the data and analyses presented
here provide a starting-place for further critical reflection on the evolution
of the history of biology over the past half-century.Comment: 45 pages, 14 figures, 4 table
The Mean and Scatter of the Velocity Dispersion-Optical Richness Relation for maxBCG Galaxy Clusters
The distribution of galaxies in position and velocity around the centers of
galaxy clusters encodes important information about cluster mass and structure.
Using the maxBCG galaxy cluster catalog identified from imaging data obtained
in the Sloan Digital Sky Survey, we study the BCG-galaxy velocity correlation
function. By modeling its non-Gaussianity, we measure the mean and scatter in
velocity dispersion at fixed richness. The mean velocity dispersion increases
from 202+/-10 km/s for small groups to more than 854+/-102 km/s for large
clusters. We show the scatter to be at most 40.5+/-3.5%, declining to
14.9+/-9.4% in the richest bins. We test our methods in the C4 cluster catalog,
a spectroscopic cluster catalog produced from the Sloan Digital Sky Survey DR2
spectroscopic sample, and in mock galaxy catalogs constructed from N-body
simulations. Our methods are robust, measuring the scatter to well within
one-sigma of the true value, and the mean to within 10%, in the mock catalogs.
By convolving the scatter in velocity dispersion at fixed richness with the
observed richness space density function, we measure the velocity dispersion
function of the maxBCG galaxy clusters. Although velocity dispersion and
richness do not form a true mass-observable relation, the relationship between
velocity dispersion and mass is theoretically well characterized and has low
scatter. Thus our results provide a key link between theory and observations up
to the velocity bias between dark matter and galaxies.Comment: 25 pages, 15 figures, 2 tables, published in Ap
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The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.
Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge
Cross-correlation Weak Lensing of SDSS galaxy Clusters II: Cluster Density Profiles and the Mass--Richness Relation
We interpret and model the statistical weak lensing measurements around
130,000 groups and clusters of galaxies in the Sloan Digital Sky Survey
presented by Sheldon et al. 2007 (Paper I). We present non-parametric
inversions of the 2D shear profiles to the mean 3D cluster density and mass
profiles in bins of both optical richness and cluster i-band luminosity. We
correct the inferred 3D profiles for systematic effects, including non-linear
shear and the fact that cluster halos are not all precisely centered on their
brightest galaxies. We also model the measured cluster shear profile as a sum
of contributions from the brightest central galaxy, the cluster dark matter
halo, and neighboring halos. We infer the relations between mean cluster virial
mass and optical richness and luminosity over two orders of magnitude in
cluster mass; the virial mass at fixed richness or luminosity is determined
with a precision of 13% including both statistical and systematic errors. We
also constrain the halo concentration parameter and halo bias as a function of
cluster mass; both are in good agreement with predictions of LCDM models. The
methods employed here will be applicable to deeper, wide-area optical surveys
that aim to constrain the nature of the dark energy, such as the Dark Energy
Survey, the Large Synoptic Survey Telescope and space-based surveys
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