Co-occurrence frequency in vegetation patches decreases towards the harsh edge along an arid volcanic elevational gradient

Positive plant-plant interactions are thought to drive vegetation patterns in harsh environments, such as semi-arid areas. According to the stress-gradient hypothesis (SGH), the role of positive interactions between species (facilitation) is expected to increase with harshness, predicting associated variation in species composition along environmental gradients. However, the relation between stress and facilitation along environmental gradients is debated. Furthermore, differentiating facilitative interactions from other underlying mechanisms, such as microtopographic heterogeneity, is not trivial. We analysed the spatial cooccurrence relationships of vascular plant species that form patchy vegetation in arid lapilli fields (tephra) from recent volcanic eruptions on La Palma, Canary Islands. We assume a harshness gradient negatively correlated with elevation because of more arid conditions at lower elevations where water availability is considered the most limiting resource. Based on the SGH we expect a greater degree of co-occurrence at lower elevations, as an outcome of facilitation is plants co-occurring in the same patch. We tested this at both the species and the individual plant level. We analysed the species composition of 1277 shrubby vegetation patches at 64 different sampling points, ranging from the coast to around 700 m a.s.l. Patch morphology and microtopographic heterogeneity variables were also measured, to account for their potential effects on the species composition of patches. We used generalized linear models and generalized mixed-effects models to analyse species richness, number of individuals in patches and percentage of patches with positive co-occurrences, and a pairwise co-occurrence analysis combined with a graphical network analysis to reveal positive links between 13 of the species. We found that the percentage of patches with positive co-occurrences increased at higher elevations, in contrast to the predictions of the SGH, but in accordance with a refined stress-gradient hypothesis for arid sites, in which characteristics of the interacting species are incorporated

Determinants of Adjective-Noun Plausibility

This paper explores the determinants of adjective-noun plausibility by using correlation analysis to compare judgements elicited from human subjects with five corpus-based variables: co-occurrence frequency of the adjective-noun pair, noun frequency, conditional probability of the noun given the adjective, the log-likelihood ratio, and Resnik’s (1993) selectional association measure. The highest correlation is obtained with the co-occurrence frequency, which points to the strongly lexicalist and collocational nature of adjective-noun combinations.

Assessing the impact of co-occurrence frequency and diversity in statistical learning accounts of language processing

Language is heralded as one of the foremost human achievements and is vital in scaffolding the successful development of many other skills. Yet, the mechanism by which language is acquired is still poorly understood. One possible account is Statistical Learning Theory, an explanation of language acquisition that has grown in popularity over the past three decades. The central tenet of Statistical Learning Theory is that learners are guided by statistical regularities in their environment and can utilise these to develop an implicit understanding of their natural language. Current theory holds that transitional probabilities are the best predictor of learner performance in statistical learning tasks. However, little has been done to investigate alternative statistical measures. This thesis presents two such metrics: Bigram frequency and bigram diversity and contrasts them with transitional probability in predicting task performance. Through the repurposing of primed lexical decision and sequence learning tasks, I present a novel approach to examining the impact of statistical priming on task performance in a naturalistic dataset. Model comparison using Bayesian multilevel modelling suggests that transitional probability is not as reliable a predictor as was previously believed. Moreover, I demonstrate that bigram frequency may represent a better metric for predicting task performance in these tasks. The current work highlights the importance of considering alternative metrics of statistical regularity when describing the underlying mechanisms of language acquisition and showcases alternative methods of examining statistical learning performance

When the “Tabula” is Anything but “Rasa:” What Determines Performance in the Auditory Statistical Learning Task?

First published: 05 February 2022How does prior linguistic knowledge modulate learning in verbal auditory statistical learning (SL) tasks? Here, we address this question by assessing to what extent the frequency of syllabic co-occurrences in the learners’ native language determines SL performance. We computed the frequency of co-occurrences of syllables in spoken Spanish through a transliterated corpus, and used this measure to construct two artificial familiarization streams. One stream was constructed by embedding pseudowords with high co-occurrence frequency in Spanish (“Spanish-like” condition), the other by embedding pseudowords with low co-occurrence frequency (“Spanish-unlike” condition). Native Spanish-speaking participants listened to one of the two streams, and were tested in an old/new identification task to examine their ability to discriminate the embedded pseudowords from foils. Our results show that performance in the verbal auditory SL (ASL) task was significantly influenced by the frequency of syllabic co-occurrences in Spanish: When the embedded pseudowords were more “Spanish-like,” participants were better able to identify them as part of the stream. These findings demonstrate that learners’ task performance in verbal ASL tasks changes as a function of the artificial language's similarity to their native language, and highlight how linguistic prior knowledge biases the learning of regularities.This paper was supported by the ERC Advanced Grant (project 692502, L2STAT), awarded to Ram Frost under the Horizon 2020 research and innovation program

A Model-Based Frequency Constraint for Mining Associations from Transaction Data

Mining frequent itemsets is a popular method for finding associated items in databases. For this method, support, the co-occurrence frequency of the items which form an association, is used as the primary indicator of the associations's significance. A single user-specified support threshold is used to decided if associations should be further investigated. Support has some known problems with rare items, favors shorter itemsets and sometimes produces misleading associations. In this paper we develop a novel model-based frequency constraint as an alternative to a single, user-specified minimum support. The constraint utilizes knowledge of the process generating transaction data by applying a simple stochastic mixture model (the NB model) which allows for transaction data's typically highly skewed item frequency distribution. A user-specified precision threshold is used together with the model to find local frequency thresholds for groups of itemsets. Based on the constraint we develop the notion of NB-frequent itemsets and adapt a mining algorithm to find all NB-frequent itemsets in a database. In experiments with publicly available transaction databases we show that the new constraint provides improvements over a single minimum support threshold and that the precision threshold is more robust and easier to set and interpret by the user

Co-occurrence frequency in vegetation patches decreases towards the harsh edge along an arid volcanic elevational gradienta

none9siPositive plant-plant interactions are thought to drive vegetation patterns in harsh environments, such as semi-arid areas. According to the stress-gradient hypothesis (SGH), the role of positive interactions between species (facilitation) is expected to increase with harshness, predicting associated variation in species composition along environmental gradients. However, the relation between stress and facilitation along environmental gradients is debated. Furthermore, differentiating facilitative interactions from other underlying mechanisms, such as microtopographic heterogeneity, is not trivial. We analysed the spatial cooccurrence relationships of vascular plant species that form patchy vegetation in arid lapilli fields (tephra) from recent volcanic eruptions on La Palma, Canary Islands. We assume a harshness gradient negatively correlated with elevation because of more arid conditions at lower elevations where water availability is considered the most limiting resource. Based on the SGH we expect a greater degree of co-occurrence at lower elevations, as an outcome of facilitation is plants co-occurring in the same patch. We tested this at both the species and the individual plant level. We analysed the species composition of 1277 shrubby vegetation patches at 64 different sampling points, ranging from the coast to around 700 m a.s.l. Patch morphology and microtopographic heterogeneity variables were also measured, to account for their potential effects on the species composition of patches. We used generalized linear models and generalized mixed-effects models to analyse species richness, number of individuals in patches and percentage of patches with positive co-occurrences, and a pairwise co-occurrence analysis combined with a graphical network analysis to reveal positive links between 13 of the species. We found that the percentage of patches with positive co-occurrences increased at higher elevations, in contrast to the predictions of the SGH, but in accordance with a refined stress-gradient hypothesis for arid sites, in which characteristics of the interacting species are incorporated.openEibes P.M.; Eisenbacher J.; Beierkuhnlein C.; Chiarucci A.; Field R.; Jentsch A.; Kohler T.; Vetaas O.R.; Irl S.D.H.Eibes P.M.; Eisenbacher J.; Beierkuhnlein C.; Chiarucci A.; Field R.; Jentsch A.; Kohler T.; Vetaas O.R.; Irl S.D.H

Research on Marine Sciences under Core University Marine Science Program in the Period of 2001-2005 : The Bibliometrics Approach

This research aims to analyze the contribution of collaboration research under Core University Marine Science Program sponsored by Japan Society for Promotion of Sciences (JSPS) over a period of 2001-2005. The data were papers of JSPS seminars. We used co-word technique of bibliometrics methods to identify the research topics. Then we calculated which topics are core and prominence levels based on method of Sujit Battarcharja and Moh’d Taiyab Rashid Khan. In-depth interviews were also carried out to explore problems on Indonesian marine research besides getting confirmation, comments and ideas related to the result of bibliometrics analysis. The informants were experts in marine science from a research institution, a government ministry, and a university. The results of this study could be employed to evaluate the research collaboration program between LIPI and Japanese institutions especially under JSPS program in the future