Language Usage and Second Language Morphosyntax: Effects of Availability, Reliability, and Formulaicity.

A large body of psycholinguistic research demonstrates that both language processing and language acquisition are sensitive to the distributions of linguistic constructions in usage. Here we investigate how statistical distributions at different linguistic levels - morphological and lexical (Experiments 1 and 2), and phrasal (Experiment 2) - contribute to the ease with which morphosyntax is processed and produced by second language learners. We analyze Chinese ESL learners' knowledge of four English inflectional morphemes: -ed, -ing, and third-person -s on verbs, and plural -s on nouns. In Elicited Imitation Tasks, participants listened to length- and difficulty-matched sentences each containing one target morpheme and typed the whole sentence as accurately as they could after a short delay. Experiment 1 investigated lexical and morphemic levels, testing the hypotheses that a morpheme is expected to be more easily processed when it is (1) highly available (i.e., occurring in frequent word-forms), and (2) highly reliable (i.e., occurring in lemma words that are consistently conjugated in the form containing this morpheme). Thirty sentences were made for each morpheme, divided into three Availability-Reliability Distribution (ARD) groups on the basis of corpus analysis in the Corpus of Contemporary American English (COCA; Davies, 2008-): 10 target words high in availability, 10 high in reliability, and 10 low in both reliability and availability. Responses were scored on whether the target morpheme was accurately reproduced given the provision of the correct lemma. A generalized linear mixed-effects logit model (GLMM) revealed fixed effects of morpheme type, availability, and reliability on the accuracy of morpheme provision. There were no effects of lemma frequency. Experiment 2 successfully replicated these results and extended the investigation to explore phrasal formulaicity by manipulating the frequency of the four-word strings in which the morpheme was embedded. GLMMs replicated the effects of word-form availability and reliability and additionally revealed independent phrase-superiority effects where morphemes were better reproduced in contexts of higher string-frequency. Taken together, these findings demonstrate that morpheme acquisition reflects the distributional properties of learners' experience and the mappings therein between lexis, morphology, phraseology, and semantics. These conclusions support an emergentist view of the statistical symbolic learning of morphology where language acquisition involves the satisfaction of competing constraints across multiple grain-sizes of units.

Global analysis of depletion and recovery of seabed biota after bottom trawling disturbance.

Bottom trawling is the most widespread human activity affecting seabed habitats. Here, we collate all available data for experimental and comparative studies of trawling impacts on whole communities of seabed macroinvertebrates on sedimentary habitats and develop widely applicable methods to estimate depletion and recovery rates of biota after trawling. Depletion of biota and trawl penetration into the seabed are highly correlated. Otter trawls caused the least depletion, removing 6% of biota per pass and penetrating the seabed on average down to 2.4 cm, whereas hydraulic dredges caused the most depletion, removing 41% of biota and penetrating the seabed on average 16.1 cm. Median recovery times posttrawling (from 50 to 95% of unimpacted biomass) ranged between 1.9 and 6.4 y. By accounting for the effects of penetration depth, environmental variation, and uncertainty, the models explained much of the variability of depletion and recovery estimates from single studies. Coupled with large-scale, high-resolution maps of trawling frequency and habitat, our estimates of depletion and recovery rates enable the assessment of trawling impacts on unprecedented spatial scales.

Thinking About Multiword Constructions: Usage-Based Approaches to Acquisition and Processing.

Usage-based approaches to language hold that we learn multiword expressions as patterns of language from language usage, and that knowledge of these patterns underlies fluent language processing. This paper explores these claims by focusing upon verb-argument constructions (VACs) such as "V(erb) about n(oun phrase)." These are productive constructions that bind syntax, lexis, and semantics. It presents (a) analyses of usage patterns of English VACs in terms of their grammatical form, semantics, lexical constituency, and distribution patterns in large corpora; (b) patterns of VAC usage in child-directed speech and child language acquisition; and (c) investigations of VAC free-association and psycholinguistic studies of online processing. We conclude that VACs are highly patterned in usage, that this patterning drives language acquisition, and that language processing is sensitive to the forms of the syntagmatic construction and their distributional statistics, the contingency of their association with meaning, and spreading activation and prototypicality effects in semantic reference. Language users have rich implicit knowledge of the statistics of multiword sequences.

Salience in Second Language Acquisition: Physical Form, Learner Attention, and Instructional Focus.

We consider the role of physical form, prior experience, and form focused instruction (FFI) in adult language learning. (1) When presented with competing cues to interpretation, learners are more likely to attend to physically more salient cues in the input. (2) Learned attention is an associative learning phenomenon where prior-learned cues block those that are experienced later. (3) The low salience of morphosyntactic cues can be overcome by FFI, which leads learners to attend cues which might otherwise be ignored. Experiment 1 used eye-tracking to investigate how language background influences learners' attention to morphological cues, as well as the attentional processes whereby different types of FFI overcome low cue salience, learned attention and blocking. Chinese native speakers (no L1 verb-tense morphology) viewed Latin utterances combining lexical and morphological cues to temporality under control conditions (CCs) and three types of explicit FFI: verb grammar instruction (VG), verb salience with textual enhancement (VS), and verb pretraining (VP), and their use of these cues was assessed in a subsequent comprehension test. CC participants were significantly more sensitive to the adverbs than verb morphology. Instructed participants showed greater sensitivity to the verbs. These results reveal attentional processes whereby learners' prior linguistic experience can shape their attention toward cues in the input, and whereby FFI helps learners overcome the long-term blocking of verb-tense morphology. Experiment 2 examined the role of modality of input presentation - aural or visual - in L1 English learners' attentional focus on morphological cues and the effectiveness of different FFI manipulations. CC participants showed greater sensitivity toward the adverb cue. FFI was effective in increasing attention to verb-tense morphology, however, the processing of morphological cues was considerably more difficult under aural presentation. From visual exposure, the FFI conditions were broadly equivalent at tuning attention to the morphology, although VP resulted in balanced attention to both cues. The effectiveness of morphological salience-raising varied across modality: VS was effective under visual exposure, but not under aural exposure. From aural exposure, only VG was effective. These results demonstrate how salience in physical form, learner attention, and instructional focus all variously affect the success of L2 acquisition.

Drivers influencing adaptive management: a retrospective evaluation of water quality decisions in South East Queensland (Australia).

This article analyzes interviews with natural resource managers in South East Queensland (SEQ), Australia. The objectives of the research are (i) to apply and test deductive/inductive text analysis methods for constructing a conceptual model of water quality decision-making in SEQ, (ii) to understand the role of information in the decision-making process, and (iii) to understand how to improve adaptive management in SEQ. Our methodology provided the means to quickly and objectively explore interview data and also reduce potential subjective bias normally associated with deductive text analysis methods. At a more practical level, our methodology indicates potential intervention points if one is to influence the decision-making process in the region. Results indicate that relevant information is often ignored in SEQ, with significant consequences for adaptive management. Contextual factors (political, social, and environmental) together with effective communication or lobbying strategies often prevent evidence-based decisions. We propose that in addition to generating information to support decisions, adaptive management also requires an appraisal of the true character of the decision-making process, which includes how stakeholders interact, what information is relevant and salient to management, and how the available information should be communicated to stakeholders and decision-making bodies.

Congruence in demersal fish, macroinvertebrate, and macroalgal community turnover on shallow temperate reefs.

To support coastal planning through improved understanding of patterns of biotic and abiotic surrogacy at broad scales, we used gradient forest modeling (GFM) to analyze and predict spatial patterns of compositional turnover of demersal fishes, macroinvertebrates, and macroalgae on shallow, temperate Australian reefs. Predictive models were first developed using environmental surrogates with estimates of prediction uncertainty, and then the efficacy of the three assemblages as biosurrogates for each other was assessed. Data from underwater visual surveys of subtidal rocky reefs were collected from the southeastern coastline of continental Australia (including South Australia and Victoria) and the northern coastline of Tasmania. These data were combined with 0.01 degree-resolution gridded environmental variables to develop statistical models of compositional turnover (beta diversity) using GFM. GFM extends the machine learning, ensemble tree-based method of random forests (RF), to allow the simultaneous modeling of multiple taxa. The models were used to generate predictions of compositional turnover for each of the three assemblages within unsurveyed areas across the 6600 km of coastline in the region of interest. The most important predictor for all three assemblages was variability in sea surface temperature (measured as standard deviation from measures taken interannually). Spatial predictions of compositional turnover within unsurveyed areas across the region of interest were remarkably congruent across the three taxa. However, the greatest uncertainty in these predictions varied in location among the different assemblages. Pairwise congruency comparisons of observed and predicted turnover among the three assemblages showed that invertebrate and macroalgal biodiversity were most similar, followed by fishes and macroalgae, and lastly fishes and invertebrate biodiversity, suggesting that of the three assemblages, macroalgae would make the best biosurrogate for both invertebrate and fish compositional turnover.

Exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional-scale ecosystems.

1. Environmental variables are often used as indirect surrogates for mapping biodiversity because species survey data are scant at regional scales, especially in the marine realm. However, environmental variables are measured on arbitrary scales unlikely to have simple, direct relationships with biological patterns. Instead, biodiversity may respond nonlinearly and to interactions between environmental variables.2. To investigate the role of the environment in driving patterns of biodiversity composition in large marine regions, we collated multiple biological survey and environmental data sets from tropical NE Australia, the deep Gulf of Mexico and the temperate Gulf of Maine. We then quantified the shape and magnitude of multispecies responses along >30 environmental gradients and the extent to which these variables predicted regional distributions. To do this, we applied a new statistical approach, Gradient Forest, an extension of Random Forest, capable of modelling nonlinear and threshold responses.3. The regional-scale environmental variables predicted an average of 13-35% (up to 50-85% for individual species) of the variation in species abundance distributions. Important predictors differed among regions and biota and included depth, salinity, temperature, sediment composition and current stress. The shapes of responses along gradients also differed and were nonlinear, often with thresholds indicative of step changes in composition. These differing regional responses were partly due to differing environmental indicators of bioregional boundaries and, given the results to date, may indicate limited scope for extrapolating bio-physical relationships beyond the region of source data sets.4.Synthesis and applications. Gradient Forest offers a new capability for exploring relationships between biodiversity and environmental gradients, generating new information on multispecies responses at a detail not available previously. Importantly, given the scarcity of data, Gradient Forest enables the combined use of information from disparate data sets. The gradient response curves provide biologically informed transformations of environmental layers to predict and map expected patterns of biodiversity composition that represent sampled composition better than uninformed variables. The approach can be applied to support marine spatial planning and management and has similar applicability in terrestrial realms.Gradient Forest offers a new capability for exploring relationships between biodiversity and environmental gradients, generating new information on multispecies responses at a detail not available previously. Importantly, given the scarcity of data, Gradient Forest enables the combined use of information from disparate data sets. The gradient response curves provide biologically informed transformations of environmental layers to predict and map expected patterns of biodiversity composition that represent sampled composition better than uninformed variables. The approach can be applied to support marine spatial planning and management and has similar applicability in terrestrial realms.

Gradient forests: calculating importance gradients on physical predictors.

In ecological analyses of species and community distributions there is interest in the nature of their responses to environmental gradients and in identifying the most important environmental variables, which may be used for predicting patterns of biodiversity. Methods such as random forests already exist to assess predictor importance for individual species and to indicate where along gradients abundance changes. However, there is a need to extend these methods to whole assemblages, to establish where along the range of these gradients the important compositional changes occur, and to identify any important thresholds or change points. We develop such a method, called "gradient forest," which is an extension of the random forest approach. By synthesizing the cross-validated R2 and accuracy importance measures from univariate random forest analyses across multiple species, sampling devices, and surveys, gradient forest obtains a monotonic function of each predictor that represents the compositional turnover along the gradient of the predictor. When applied to a synthetic data set, the method correctly identified the important predictors and delineated where the compositional change points occurred along these gradients. Application of gradient forest to a real data set from part of the Great Barrier Reef identified mud fraction of the sediment as the most important predictor, with highest compositional turnover occurring at mud fraction values around 25%, and provided similar information for other predictors. Such refined information allows for more accurate capturing of biodiversity patterns for the purposes of bioregionalization, delineation of protected areas, or designing of biodiversity surveys.

An empirical model of water quality for use in rapid management strategy evaluation in Southeast Queensland, Australia.

Simulation models have been widely adopted in fisheries for management strategy evaluation (MSE). However, in catchment management of water quality, MSE is hampered by the complexity of both decision space and the hydrological process models. Empirical models based on monitoring data provide a feasible alternative to process models; they run much faster and, by conditioning on data, they can simulate realistic responses to management actions. Using 10 years of water quality indicators from Queensland, Australia, we built an empirical model suitable for rapid MSE that reproduces the water quality variables' mean and covariance structure, adjusts the expected indicators through local management effects, and propagates effects downstream by capturing inter-site regression relationships. Empirical models enable managers to search the space of possible strategies using rapid assessment. They provide not only realistic responses in water quality indicators but also variability in those indicators, allowing managers to assess strategies in an uncertain world.