2,241 research outputs found
Prevalence, intensity, and effect of a nematode (Philometra saltatrix) in the ovaries of bluefish (Pomatomus saltatrix)
Examination of 203 adult bluefish (Pomatomus saltatrix) from
Long Island, New York, in 2002 and 2003 and 66 from the Outer Banks, North Carolina, in 2003 revealed the presence of dracunculoid nematodes (Philometra saltatrix) in the ovaries of female fish. Percent prevalence reached 88% in July and then decreased after the peak of the spawning season. Bluefish contained up to 100 parasites per fish. Infection was associated with a range of disorders, including hemorrhage, inf lammation, edema, prenecrotic
and necrotic changes, and follicular atresia, that may prevent proper development of oocytes and probably affect bluefish fecundity. Historical occurrences, life cycle, and geographical distribution of this nematode remain largely unknown, but may play important roles in recruitment
processes of bluefish
High connectivity among locally adapted populations of a marine fish (Menidia menidia)
Author Posting. © Ecological Society of America, 2010. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecology 91 (2010): 3526–3537, doi:10.1890/09-0548.1.Patterns of connectivity are important in understanding the geographic scale of local adaptation in marine populations. While natural selection can lead to local adaptation, high connectivity can diminish the potential for such adaptation to occur. Connectivity, defined as the exchange of individuals among subpopulations, is presumed to be significant in most marine species due to life histories that include widely dispersive stages. However, evidence of local adaptation in marine species, such the Atlantic silverside, Menidia menidia, raises questions concerning the degree of connectivity. We examined geochemical signatures in the otoliths, or ear bones, of adult Atlantic silversides collected in 11 locations along the northeastern coast of the United States from New Jersey to Maine in 2004 and eight locations in 2005 using laser ablation inductively coupled plasma mass spectrometry (ICP-MS) and isotope ratio monitoring mass spectrometry (irm-MS). These signatures were then compared to baseline signatures of juvenile fish of known origin to determine natal origin of these adult fish. We then estimated migration distances and the degree of mixing from these data. In both years, fish generally had the highest probability of originating from the same location in which they were captured (0.01–0.80), but evidence of mixing throughout the sample area was present. Furthermore, adult M. menidia exhibit highly dispersive behavior with some fish migrating over 700 km. The probability of adult fish returning to natal areas differed between years, with the probability being, on average, 0.2 higher in the second year. These findings demonstrate that marine species with largely open populations are capable of local adaptation despite apparently high gene flow.This work was funded by the National Science Foundation
(grant OCE-0425830 to D. O. Conover and grant OCE-
0134998 to S. R. Thorrold) and the New York State
Department of Environmental Conservation
Partisan Asymmetries in Online Political Activity
We examine partisan differences in the behavior, communication patterns and
social interactions of more than 18,000 politically-active Twitter users to
produce evidence that points to changing levels of partisan engagement with the
American online political landscape. Analysis of a network defined by the
communication activity of these users in proximity to the 2010 midterm
congressional elections reveals a highly segregated, well clustered partisan
community structure. Using cluster membership as a high-fidelity (87% accuracy)
proxy for political affiliation, we characterize a wide range of differences in
the behavior, communication and social connectivity of left- and right-leaning
Twitter users. We find that in contrast to the online political dynamics of the
2008 campaign, right-leaning Twitter users exhibit greater levels of political
activity, a more tightly interconnected social structure, and a communication
network topology that facilitates the rapid and broad dissemination of
political information.Comment: 17 pages, 10 figures, 6 table
Portraits of Complex Networks
We propose a method for characterizing large complex networks by introducing
a new matrix structure, unique for a given network, which encodes structural
information; provides useful visualization, even for very large networks; and
allows for rigorous statistical comparison between networks. Dynamic processes
such as percolation can be visualized using animations. Applications to graph
theory are discussed, as are generalizations to weighted networks, real-world
network similarity testing, and applicability to the graph isomorphism problem.Comment: 6 pages, 9 figure
A Group-Based Yule Model for Bipartite Author-Paper Networks
This paper presents a novel model for author-paper networks, which is based
on the assumption that authors are organized into groups and that, for each
research topic, the number of papers published by a group is based on a
success-breeds-success model. Collaboration between groups is modeled as random
invitations from a group to an outside member. To analyze the model, a number
of different metrics that can be obtained in author-paper networks were
extracted. A simulation example shows that this model can effectively mimic the
behavior of a real-world author-paper network, extracted from a collection of
900 journal papers in the field of complex networks.Comment: 13 pages (preprint format), 7 figure
A meta-analysis of state-of-the-art electoral prediction from Twitter data
Electoral prediction from Twitter data is an appealing research topic. It
seems relatively straightforward and the prevailing view is overly optimistic.
This is problematic because while simple approaches are assumed to be good
enough, core problems are not addressed. Thus, this paper aims to (1) provide a
balanced and critical review of the state of the art; (2) cast light on the
presume predictive power of Twitter data; and (3) depict a roadmap to push
forward the field. Hence, a scheme to characterize Twitter prediction methods
is proposed. It covers every aspect from data collection to performance
evaluation, through data processing and vote inference. Using that scheme,
prior research is analyzed and organized to explain the main approaches taken
up to date but also their weaknesses. This is the first meta-analysis of the
whole body of research regarding electoral prediction from Twitter data. It
reveals that its presumed predictive power regarding electoral prediction has
been rather exaggerated: although social media may provide a glimpse on
electoral outcomes current research does not provide strong evidence to support
it can replace traditional polls. Finally, future lines of research along with
a set of requirements they must fulfill are provided.Comment: 19 pages, 3 table
Twitter-based analysis of the dynamics of collective attention to political parties
Large-scale data from social media have a significant potential to describe
complex phenomena in real world and to anticipate collective behaviors such as
information spreading and social trends. One specific case of study is
represented by the collective attention to the action of political parties. Not
surprisingly, researchers and stakeholders tried to correlate parties' presence
on social media with their performances in elections. Despite the many efforts,
results are still inconclusive since this kind of data is often very noisy and
significant signals could be covered by (largely unknown) statistical
fluctuations. In this paper we consider the number of tweets (tweet volume) of
a party as a proxy of collective attention to the party, identify the dynamics
of the volume, and show that this quantity has some information on the
elections outcome. We find that the distribution of the tweet volume for each
party follows a log-normal distribution with a positive autocorrelation of the
volume over short terms, which indicates the volume has large fluctuations of
the log-normal distribution yet with a short-term tendency. Furthermore, by
measuring the ratio of two consecutive daily tweet volumes, we find that the
evolution of the daily volume of a party can be described by means of a
geometric Brownian motion (i.e., the logarithm of the volume moves randomly
with a trend). Finally, we determine the optimal period of averaging tweet
volume for reducing fluctuations and extracting short-term tendencies. We
conclude that the tweet volume is a good indicator of parties' success in the
elections when considered over an optimal time window. Our study identifies the
statistical nature of collective attention to political issues and sheds light
on how to model the dynamics of collective attention in social media.Comment: 16 pages, 7 figures, 3 tables. Published in PLoS ON
Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok
TikTok is a video-sharing social networking service, whose popularity is
increasing rapidly. It was the world's second-most downloaded app in 2019.
Although the platform is known for having users posting videos of themselves
dancing, lip-syncing, or showcasing other talents, user-videos expressing
political views have seen a recent spurt. This study aims to perform a primary
evaluation of political communication on TikTok. We collect a set of US
partisan Republican and Democratic videos to investigate how users communicated
with each other about political issues. With the help of computer vision,
natural language processing, and statistical tools, we illustrate that
political communication on TikTok is much more interactive in comparison to
other social media platforms, with users combining multiple information
channels to spread their messages. We show that political communication takes
place in the form of communication trees since users generate branches of
responses to existing content. In terms of user demographics, we find that
users belonging to both the US parties are young and behave similarly on the
platform. However, Republican users generated more political content and their
videos received more responses; on the other hand, Democratic users engaged
significantly more in cross-partisan discussions.Comment: Accepted as a full paper at the 12th International ACM Web Science
Conference (WebSci 2020). Please cite the WebSci version; Second version
includes corrected typo
Geometric and dynamic perspectives on phase-coherent and noncoherent chaos
Statistically distinguishing between phase-coherent and noncoherent chaotic
dynamics from time series is a contemporary problem in nonlinear sciences. In
this work, we propose different measures based on recurrence properties of
recorded trajectories, which characterize the underlying systems from both
geometric and dynamic viewpoints. The potentials of the individual measures for
discriminating phase-coherent and noncoherent chaotic oscillations are
discussed. A detailed numerical analysis is performed for the chaotic R\"ossler
system, which displays both types of chaos as one control parameter is varied,
and the Mackey-Glass system as an example of a time-delay system with
noncoherent chaos. Our results demonstrate that especially geometric measures
from recurrence network analysis are well suited for tracing transitions
between spiral- and screw-type chaos, a common route from phase-coherent to
noncoherent chaos also found in other nonlinear oscillators. A detailed
explanation of the observed behavior in terms of attractor geometry is given.Comment: 12 pages, 13 figure
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