Resilience of a tropical sport fish population to a severe cold event varies across five estuaries in southern Florida

For species that are closely managed, understanding population resilience to environmental and anthropogenic disturbances (i.e., recovery trajectories across broad spatial areas) can guide which suite of management actions are available to mitigate any impacts. During January 2010, an extreme cold event in south Florida caused widespread mortality of common snook, Centropomus undecimalis, a popular sport fish. Interpretation of trends using fishery-independent monitoring data in five south Florida estuaries showed that changes in catch rates of adult snook (\u3e500 mm standard length) varied between no effects postevent to large effects and 4-yr recoveries. The reasons for the variation across estuaries are unknown, but are likely related to differences in estuary geomorphology and habitat availability (e.g., extent of deep rivers and canals) and differences in the proportions of behavior contingents (i.e., segments of the population that use divergent movement tactics) that place snook in different areas of the estuary during winter. Emerging awareness of the presence of behavior contingents, identification of overwintering sites, and improvements of abundance indices in remote nursery habitats should provide a better understanding of population resilience to disturbance events for snook. Given that changes in the frequency of short-lived, severe cold events are currently unknown, the findings and management actions described here for a tropical species living at the edge of its distribution should be useful to scientists forecasting the effects of climate change

Knocking back invasions: variable resistance and resilience to multiple cold spells in native vs. nonnative fishes

Extreme climate events can interact synergistically with invasions to dramatically alter ecosystem structure, function, and services. Yet, the effects of extreme climate events on species invasions remain unresolved. Extreme climate events may increase resources and decrease biotic resistance by causing physiological stress and/or mortality of native taxa, resulting in invasion opportunities for nonnative species. Alternatively, extreme climate events may regulate nonnative populations, preventing them from achieving dominance. We examined whether a sequence of three cold spells had a negative or positive effect on fish invasions in the coastal Everglades. We compared resistance (initial effects) and resilience (rate of recovery) to the cold spells between native fishes and the dominant nonnative invader, the Mayan cichlid, across eight populations expanding two mangroves drainages in the southern Everglades. We tracked native fish and nonnative Mayan cichlid populations for 10 yr including 3 yr pre- and 4 yr post-cold spells. In both drainages, native fishes were more resistant to the cold spells than the nonnative species. While native fishes experienced declines at only one site, nonnative Mayan numbers were reduced by 90–100% across six sites where they were abundant pre-disturbances. Four years after the last cold spell, we saw limited resilience in the affected nonnative populations. Only one of the six affected sites fully recovered, whereas the other five sites showed no recovery in Mayan cichlid numbers. The recovered site was closest to a canal, known to act as thermal refuges for nonnative fishes. In summary, cold spells can reduce nonnative abundances, but whether cold spells can effectively knock back invasions (and range expansions) by tropical/subtropical nonnative species will depend on how the frequency and severity of cold spells are affected by climate change. We propose that these mortality-causing extreme events could provide rare management opportunities late in an invasion

The LAMBADA dataset: word prediction requiring a broad discourse context

We introduce LAMBADA, a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. We show that LAMBADA exemplifies a wide range of linguistic phenomena, and that none of several state-ofthe-art language models reaches accuracy above 1% on this novel benchmark. We thus propose LAMBADA as a challenging test set, meant to encourage the development of new models capable of genuine understanding of broad context in natural language text.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 655577 (LOVe); ERC 2011 Starting Independent Research Grant n. 283554 (COMPOSES); NWO VIDI grant n. 276-89-008 (Asymmetry in Conversation)

A Structured Distributional Model of Sentence Meaning and Processing

International audienceMost compositional distributional semantic models represent sentence meaning with a single vector. In this paper, we propose a Structured Distributional Model (SDM) that combines word embeddings with formal semantics and is based on the assumption that sentences represent events and situations. The semantic representation of a sentence is a formal structure derived from Discourse Representation Theory and containing distri-butional vectors. This structure is dynamically and incrementally built by integrating knowledge about events and their typical participants, as they are activated by lexical items. Event knowledge is modeled as a graph extracted from parsed corpora and encoding roles and relationships between participants that are represented as distributional vectors. SDM is grounded on extensive psycholinguistic research showing that generalized knowledge about events stored in semantic memory plays a key role in sentence comprehension. We evaluate SDM on two recently introduced compositionality datasets, and our results show that combining a simple compositional model with event knowledge constantly improves performances, even with different types of word embeddings. 1 Sentence Meaning in Vector Spaces While for decades sentence meaning has been represented in terms of complex formal structures, the most recent trend in computational semantics is to model semantic representations with dense distributional vectors (aka embeddings). As a matter of fact, distributional semantics has become one of the most influential approaches to lexical meaning, because of the important theoretical and computational advantages of representing words with continuous vectors, such as automatically learning lexical representations from natural language corpora and multimodal data, assessing semantic similarity in terms of the distance between the vectors, and dealing with the inherently gradient and fuzzy nature of meaning (Erk 2012, Lenci 2018a)