Range Shifts of Overwintering Birds Depend on Habitat Type, Snow Conditions and Habitat Specialization

Climatic warming is forcing numerous species to shift their ranges poleward, which has been demonstrated for many taxa across the globe. Yet, the influence of habitat types on within- and among-species variations of distribution shifts has rarely been studied, especially so for the non-breeding season. Here, we investigated habitat specific shift distances of northern range margins and directions of the center of gravity based on a long-term dataset of overwintering birds in Finland. Specifically, we explored influences of habitat type, snow cover depths, species’ climatic niche and habitat specialization on range shifts from 1980’s to 2010’s in 81 bird species. Birds overwintering in farmlands shifted significantly more often northwards than birds of the same species in rural and forest habitats, while the northern range margin shift distances did not significantly differ among the habitat types. Snow cover was negatively associated with the eastward shift direction across all habitats, while we found habitat specific relations to snow cover with northward shift directions and northern range margins shift distances. Species with stronger habitat specializations shifted more strongly towards north as compared to generalist species, whereas the climatic niche of bird species only marginally correlated with range shifts, so that cold-dwelling species shifted longer distances and more clearly eastwards. Our study reveals habitat specific patterns linked to snow conditions for overwintering boreal birds and highlights importance of habitat availability and preference in climate driven range shifts.

Communicative Approach in Teaching Foreign Languages

The Communitive Approach in language teaching starts from а theory of language as communication. The goal of language teaching is to develop communicative competence. Another linguistic theory of communication favored in CLT is functional account of language use. Linguistic is concerned with the description of speech acts of texts, since only through study of language in use are all the functions of language and therefore all components of meaning brought into focus. The goal of language teaching is to develop what referred to as “communicative competence”. This term is coined in order to contrast а communicative view of language and theory of competence. Linguistic theory is concerned primarily with an ideal speaker-listener in а completely homogeneous speech community, who knows its language perfectly and is unaffected by such grammatically irrelevant conditions as memory limitation, distribution, shifts of attention and interest, and errors (random or characteristic) in applying his knowledge of the language in actual performance.

Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications

Uncertainties in machine learning are a significant roadblock for its application in safety-critical cyber-physical systems (CPS). One source of uncertainty arises from distribution shifts in the input data between training and test scenarios. Detecting such distribution shifts in real-time is an emerging approach to address the challenge. The high dimensional input space in CPS applications involving imaging adds extra difficulty to the task. Generative learning models are widely adopted for the task, namely out-of-distribution (OoD) detection. To improve the state-of-the-art, we studied existing proposals from both machine learning and CPS fields. In the latter, safety monitoring in real-time for autonomous driving agents has been a focus. Exploiting the spatiotemporal correlation of motion in videos, we can robustly detect hazardous motion around autonomous driving agents. Inspired by the latest advances in the Variational Autoencoder (VAE) theory and practice, we tapped into the prior knowledge in data to further boost OoD detection’s robustness. Comparison studies over nuScenes and Synthia data sets show our methods significantly improve detection capabilities of OoD factors unique to driving scenarios, 42% better than state-of-the-art approaches. Our model also generalized near-perfectly, 97% better than the state-of-the-art across the real-world and simulation driving data sets experimented. Finally, we customized one proposed method into a twin-encoder model that can be deployed to resource limited embedded devices for real-time OoD detection. Its execution time was reduced over four times in low-precision 8-bit integer inference, while detection capability is comparable to its corresponding floating-point model.

Creep Characteristics of Different Saturated States of Red Sandstone after Freeze-Thaw Cycles

To investigate the creep mechanical characteristics of rocks in different saturated states after freeze-thaw cycles, samples with different saturations (30%, 50%, 70%, 90%, and 100%) were selected for nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), and uniaxial compression creep tests. The internal microscopic damage of the rock sample and mechanical characteristics under long-term loading are analyzed after the action of freeze-thaw cycles. The test results show that, as the saturation increases, the T2 spectrum distribution shifts to the right. The spectrum area gradually increases as the porosity increases. The critical saturation of freeze-thaw damage occurs when the saturation increases from 70% to 90%. It can be seen from the SEM image that the number of pores inside the rock samples gradually increases with increases in saturation, leading to the appearance of cracks. Under long-term loading, the saturation has a significant influence on the time-efficiency characteristics of sandstone freeze-thaw. As the saturation increases, the creep deformation gradually increases. After reaching 70%, the axial creep strain increases significantly. The rate of creep is accelerated, the creep failure stress is reduced, and the creep time under the last level of stress is significantly increased. A modified viscous-plastic body is connected in series to the basic Burgers model, the freeze-thaw-damage viscous element is introduced, and the creep parameters are fitted using test data. The results will provide a reference for long-term antifreeze design for rock engineering in cold areas.

Skilful decadal-scale prediction of fish habitat and distribution shifts

Many fish and marine organisms are responding to our planet's changing climate by shifting their distribution (i.e. where they are found). Such shifts can drive conflicts at the international scale and are highly problematic for the communities and businesses that depend on these living marine resources for income and nutrition. Advances in climate prediction mean that in some regions the state of the ocean, and thereby the drivers of these shifts, can be skilfully forecast up to a decade ahead. However, the potential for these forecasts to benefit ocean-dependent communities has yet to be realised. Here we show for the first time that marine climate predictions can be used to generate decadal-scale forecasts of shifts in the habitat and distribution of marine fish species, as exemplified by Atlantic mackerel, bluefin tuna and blue whiting. We show statistically significant forecast skill of individual years that outperform both persistence and climatological baseline forecasts for lead times of 3-10 years: multi-year averages perform even better, yielding correlation coefficients in excess of 0.90 in some cases. We also show that the habitat shifts underling recent conflicts over Atlantic mackerel fishing rights could have been foreseen on similar timescales. Our results show that climate predictions can be translated into information directly relevant to stakeholders and we anticipate that this tool will be critical in foreseeing, adapting to and coping with the challenges of a changing and variable future climate, particularly in the most ocean-dependent nations and communities.

Do temperature shocks affect non-agriculture wages in Brazil? Evidence from individual-level panel data

The relationship between temperature and agriculture outcomes in Brazil has been widely explored, overlooking the fact that most of the country's labor force is employed in non-agriculture sectors. We use monthly individual-level panel data spanning the period from January 2015 to December 2016 to ask whether temperature shocks impact non-agriculture wages in formal labor markets. Our results show that additional days in a month that fall within high-temperature ranges have significant adverse effects on real wages. Assuming a uniform climate change scenario where the daily temperature distribution shifts by 2 $^{\circ }$ C, we calculate income losses for formal workers in non-agriculture markets equivalent to 0.12 per cent of 2015 GDP.