1,720,970 research outputs found
To sleep or not to sleep: dormancy and life history traits in Eucypris virens (Crustacea, Ostracoda)
No full textDormancy represents an investment with its own costs and benefit. Besides the advantage obtained from the avoidance of harsh environments and from the synchronization of life cycles with seasonal changes, an organism could benefit from a temporary stop in growth and reproduction. To test this hypothesis a transgenerational experiment was carried out comparing the life history traits of clonal females of Eucypris virens from resting and non-resting eggs at two different photoperiods: short day length (6:18 L:D), proxy of favorable but unpredictable late winter-spring hydroperiod, and long day length (16:8 L:D) proxy of dry predictable unfavorable season, inducing resting egg production and within-generation plasticity (WGP). Clonal females that were dormancy deprived showed the highest age at first deposition and the lowest fecundity. Dormancy seems to work as a resetting mechanism of reproduction. Transgenerational plasticity (TGP) had a bounce back pattern: the phenotype of F1 generation was influenced by cues experienced in the F0 generation but the effects of F0 exposure were not evident in the F2. TGP might be adaptive when a mother experiences some kind of seasonality or stochasticity producing both resting and nonresting eggs. A positive relationship between the number of resting eggs and the total number of eggs per females suggested the absence of trade-off between dormancy and reproduction. Both WGP and TGP increase the mother long term fitness with important consequences on population dynamics, on the way a species spread throughout space and time and might respond to climate change
Modeling the effects of climate change on the habitat suitability of Mediterranean gorgonians
No full textMultiple stressors, including global warming, increasingly threaten the distribution and abundance of gorgonian forests. We built species distribution models (SDM) combined with machine learning algorithms to compare the ecological niche and distribution response to climate change under the worst IPCC scenario RCP8.5 for three Mediterranean gorgonian species (Paramuricea clavata,Eunicella cavolinii and Eunicella singularis. To obtain the potential habitat suitability and future distribution projections (2040-2050), we employed three Machine Learning models (XGBoost, Random Forest and the K-nearest neighbour) which considered 23 physicochemical and 4 geophysical environmental variables. The global sensitivity and uncertainty analysis was used to identify the most important environmental variables shaping habitat suitability for each species and to disentangle the interaction terms among environmental variables. For all species, bathymetry was the primary variable influencing habitat suitability, which had strong interactions with silicate concentration, salinity, and concavity. Under predicted future climatic conditions, P. clavata is predicted to shift its habitat suitability from lower to higher latitudes, mainly in the Adriatic Sea. For both E. cavolinii and E. singularis, a general habitat reduction was predicted. In particular, E. cavolinii is expected to reduce its occupancy area by 49%, suggesting that the sensitivity of symbiotic algae (zooxanthellae) may not be the principal cause of susceptibility of this species to thermal stresses and climate change
Acoustic features as a tool to visualize and explore marine soundscapes: applications illustrated using marine mammal Passive Acoustic Monitoring datasets
No full textPassive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches are best suited for characterizing marine acoustic environments. Here, we describe the application of multiple machine-learning techniques to the analysis of two PAM datasets. We combine pre-trained acoustic classification models (VGGish, NOAA and Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine acoustic environment. The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labeled sounds in the 8 kHz range; however, low- and high-frequency sounds could not be classified using this approach. The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow establishing a link between ecologically relevant information and PAM recordings at multiple scales, ranging from large-scale changes in the environment (i.e., changes in wind speed) to the identification of marine mammal species.
Our study explores the use of VGGish acoustic features and UMAP dimensionality reduction for the analysis of marine soundscapes. We combine pre-trained acoustic classification models, dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine environment.imag
Species distribution modeling and machine learning in assessing the potential distribution of freshwater zooplankton in Northern Italy
No full textSpecies distribution models (SDM's) are powerful tools used to describe species suitable habitats and spatial occurrences and many statistical methods and algorithms are available to model the spatial distribution of a target species. Here we explore a species distribution model framework combined with machine learning algorithms to describe the distribution of two freshwater zooplankton species Daphnia longispina (Cladocera) and Eucyclops serrulatus (Copepods) in a system of 283 shallow and ephemeral freshwater habitats in the Northern Italian Appennines. For each species, we model the habitat suitability by comparing one regression-based model, one generalized linear model (GLM) and two machine learning algorithms: random forest (RF) and artificial neural network (ANN) with one hidden layer. We used a total of 27 predictor variables. The modeling framework was used considering a scenario of future climate change in order to evaluate potential shifts in spatial distribution of the zooplankton species. For both species, the supervised machine learning algorthn (ANN) produced the highest mean values for all the performance metrics. For D. longispina and E. serrulatus, the two most important variables ranked by the shap analysis and global sensitivity and uncertainty analysis (GSUA) were temperature seasonality and precipitation of the warmest quarter. Both species, in a future climatic change scenario, are expected to shift their distribution mainly toward lower northern altitudes with an overall expansion of 7% with respect to the past/present climatic conditions. However, the spatial expansion of D. longispina and E. serrulatus was qualitatively different. In agricultural and natural areas, the expansion of E. serrulatus was greater than that of D. longispina but, in natural areas, the expansion of E. serrulatus was counterbalanced by a greater spatial contraction than that of D. longispina. As hypothesized, direct and indirect anthropogenic pressures may affect the predicted potential shift and expansion of the zooplankton species
Assessing the extinction risk of heterocypris incongruens (Crustacea: Ostracoda) in climate change with sensitivity and uncertainty analysis
Full text linkOrganisms respond to climate change in many different ways and their local extinction risk may vary widely among taxa. Crustaceans from freshwater temporary ponds produce resting eggs to cope with environmental uncertainty and, as a consequence, egg banks have a fundamental role for population persistence. The egg bank dynamics of six clonal lineages of Heterocypris incongruens (Ostracoda) from Northern Italy were simulated. Clonal lineages W1 and W2 are the most common “winter ecotypes”, clonal lineages S1 and S2 are allochthonous “summer ecotypes” and clonal lineages I1 and I2 are relatively rare and generalist in terms of seasonality. Fecundity and proportion of resting eggs vary by clonal lineage, temperature and photoperiod. The clonal extinction risk was estimated in present climate conditions and under climate change. For comparison, and to assess the potential colonization of northern ponds, clonal lineages from Lampedusa Island (Southern Italy), L, were considered. Cohen’s general model was used for simulating egg bank dynamics and the extinction rate of each clonal lineage was estimated with uncertainty analysis. A 30 year simulation in present and climate change conditions was carried out. Extinction rates were lower in climate change conditions than in present conditions. Hydroperiod, hatching rate and egg deterioration rate were the critical factors that affected extinction rates. Extinction rates varied among clonal lineages. This suggests that H. incongruens might be able to have multiple responses to climate change due to its genetic diversity. In climate change conditions, W clonal lineages underwent a niche expansion, while a mismatch between photoperiod and hydroperiod might generate a detrimental effect on the phenology of summer S clonal lineages that might cause their extinction. Southern clonal lineages L, showing an intermediate extinction rate, might colonize northern temporary ponds
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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