Macias McGarry (maracacopy90)

Harvest selection also had a substantial behavioral component independent of body length, while natural behavioral selection was not documented, suggesting the potential for directional harvest selection favoring inactive, timid fish. Simulations of the outcomes of different fishing regulations showed that traditional minimum size-based harvest limits are unlikely to counteract harvest selection without being completely restrictive. Our study suggests harvest selection may be inevitable and recreational fisheries may thus favor small, inactive, shy, and difficult-to-capture fish. Increasing fractions of shy fish in angling-exploited stocks would have consequences for stock assessment and all fisheries operating with hook and line.Anthropogenic climate change profoundly alters the ocean's environmental conditions, which, in turn, impact marine ecosystems. Some of these changes are happening fast and may be difficult to reverse. The identification and monitoring of such changes, which also includes tipping points, is an ongoing and emerging research effort. Prevention of negative impacts requires mitigation efforts based on feasible research-based pathways. Climate-induced tipping points are traditionally associated with singular catastrophic events (relative to natural variations) of dramatic negative impact. High-probability high-impact ocean tipping points due to warming, ocean acidification, and deoxygenation may be more fragmented both regionally and in time but add up to global dimensions. These tipping points in combination with gradual changes need to be addressed as seriously as singular catastrophic events in order to prevent the cumulative and often compounding negative societal and Earth system impacts.An influential view in philosophy and linguistics equates the meaning of a sentence to the conditions under which it is true. But it has been argued that this truth-conditional view is too rigid and that meaning is inherently gradient and revolves around prototypes. Neither of these abstract semantic theories makes direct predictions about quantitative aspects of language use. Hence, we compare these semantic theories empirically by applying probabilistic pragmatic models as a link function connecting linguistic meaning and language use. We consider the use of quantity words (e.g., "some," "all"), which are fundamental to human language and thought. Data from a large-scale production study suggest that quantity words are understood via prototypes. We formulate and compare computational models based on the two views on linguistic meaning. These models also take into account cognitive factors, such as salience and numerosity representation. Statistical and empirical model comparison show that the truth-conditional model explains the production data just as well as the prototype-based model, when the semantics are complemented by a pragmatic module that encodes probabilistic reasoning about the listener's uptake.Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, we combine the advantages of a machine-learning framework and an iterative optimization process to develop a method for integrating genetic and environmental (e.g., climate, land cover, human infrastructure) data. We validate and demonstrate this method for the Aedes aegypti mosquito, an invasive species and the primary vector of dengue, yellow fever, chikungunya, and Zika. We test two contrasting metrics to approximate genetic distance and find Cavalli-Sforza-Edwards distance (CSE) performs better than linearized FST The correlation (R) between the model's predicted genetic distance and actual distance is 0.83. We produce a map of genetic connectivity for Ae. aegypti's range in North America