A rapid altitudinal range expansion in the pine processionary moth produced by the 2003 climatic anomaly

Climatic anomalies may produce, or accelerate, geographic range expansions of species limited by temperature or other climatic variables. Most such expansions are only temporary, before the prevailing climatic conditions drive the founder populations extinct. In contrast, here, we report a recent rapid shift of the range limit during the record hot summer of 2003 in southern Europe that has the potential to be both permanent, and to have important implications on species range dynamics in general. The winter pine processionary moth (Thaumetopoea pityocampa), an important pine defoliator whose larvae feed in colonies during the winter, is limited in its distribution by winter temperatures. In the last three decades, warmer winters have led to a gradual but substantial expansion of its range both latitudinally and altitudinally. In the summer of 2003, T. pityocampa underwent an extraordinary expansion to high elevation pine stands in the Italian Alps; its altitudinal range limit increased by one third of the total altitudinal expansion over the previous three decades. In an experiment, we found flight activity of newly emerged females to increase with temperature. By determining a threshold temperature for flight take‐offs under controlled conditions, we calculated that the nights above the threshold temperature were over five times more frequent, and considerably warmer, at the range limit in 2003 than in an average year. We therefore attribute the colonization of extreme, high‐elevation sites to increased nocturnal dispersal of females during the unusually warm night temperatures in June – August 2003. Importantly, the colonies established at extreme sites survived the winter and produced offspring in 2004, although the range did not expand further because of low night temperatures that year. We discuss several life‐history characteristics of T. pityocampa that maximize the likelihood of population persistence at the new range limit. As global warming continues and climatic anomalies are predicted to become more frequent, our results draw attention to the importance of extreme climatic events in the range formation of phytophagous insects.     

Maternal induction of larval diapause and its sensitive stage in the blow fly Lucilia sericata (Meigen) (Diptera: Calliphoridae)

Lucilia sericata has a facultative diapause in the third larval instar after cessation of feeding. Induction of the diapause is influenced by the photoperiod and temperature conditions experienced by insects in the parental generation as well as those experienced by the larvae themselves. The sensitive stage of the parental generation for induction of diapause was examined using diapause‐averting conditions of 16 h light : 8 h darkness (LD 16:8) at 25°C and diapause‐inducing conditions of LD 12:12 at 20°C. The incidence of diapause in the progeny was predominantly determined by the conditions experienced by the parents in the adult stage. Moreover, the results of reciprocal crosses showed that only the mother's experience is involved in the induction of diapause in the progeny.     

Effects on temperature on spontaneous and thyroxine-stimulated locomotor activity of Atlantic cod

Spontaneous locomotor activity of cod Gadus morhua maintained at 6° C tripled from February to May. In contrast, locomotor activity of cod held at 2° C was significantly lower than at 6° C (between 25 and 65% lower) and the seasonal increase was smaller. Plasma levels of both thyroxine (T₄) and triiodothyronine (T₃) did not differ between 2 and 6° C. T₄ injection increased locomotor activity by 10% for both temperature regimes. These data indicate that low water temperature reduces locomotor activity associated with migration in cod and that thyroid hormones are not involved in this decrease. This study provides a possible mechanism through which cold waters may affects migration and distribution of cod via its Effects on locomotor activity and swimming speed.     

Contrasting population changes in sympatric penguin species in association with climate warming

Climate warming and associated sea ice reductions in Antarctica have modified habitat conditions for some species. These include the congeneric Adélie, chinstrap and gentoo penguins, which now demonstrate remarkable population responses to regional warming. However, inconsistencies in the direction of population changes between species at different study sites complicate the understanding of causal processes. Here, we show that at the South Orkney Islands where the three species breed sympatrically, the less ice‐adapted gentoo penguins increased significantly in numbers over the last 26 years, whereas chinstrap and Adélie penguins both declined. These trends occurred in parallel with regional long‐term warming and significant reduction in sea ice extent. Periodical warm events, with teleconnections to the tropical Pacific, caused cycles in sea ice leading to reduced prey biomass, and simultaneous interannual population decreases in the three penguin species. With the loss of sea ice, Adélie penguins were less buffered against the environment, their numbers fluctuated greatly and their population response was strong and linear. Chinstrap penguins, considered to be better adapted to ice‐free conditions, were affected by discrete events of locally increased ice cover, but showed less variable, nonlinear responses to sea ice loss. Gentoo penguins were temporarily affected by negative anomalies in regional sea ice, but persistent sea ice reductions were likely to increase their available niche, which is likely to be substantially segregated from that of their more abundant congeners. Thus, the regional consequences of global climate perturbations on the sea ice phenology affect the marine ecosystem, with repercussions for penguin food supply and competition for resources. Ultimately, variability in penguin populations with warming reflects the local balance between penguin adaptation to ice conditions and trophic‐mediated changes cascading from global climate forcing.     

Separating the climatic signal from tree-ring width and maximum latewood density records

We propose a technique for separating the climatic signal which is contained in two tree-ring parameters widely used in dendroclimatology. The method is based on the removal of the relationship between tree-ring width (TRW) and maximum latewood density (MXD) observed for narrow tree rings from high latitudes. The new technique is tested on data from three larch stands located along the northern timberline in Eurasia. Correlations were calculated between the temperatures of pentads (five consecutive days), TRW chronologies and MXD chronologies calculated according to the standard and proposed methods. The analysis confirms the great importance of summer temperature for tree radial growth and tree-ring formation. TRW is positively correlated with the temperature of four to eight pentads (depending on the region) at the beginning of the growth season, but MXD as obtained by the standard technique is correlated with temperature over a much longer period. For maximum density series from which the relationship between MXD and TRW has been removed (MXD′), there is a clear correlation with temperatures in the second part of the growing season. These results are consistent with the known dynamics of tree-ring growth in high latitudes and mechanisms of tree-ring formation.     

Proteins in Tumor-Derived Plasma Extracellular Vesicles Indicate Tumor Origin

Cancer-derived extracellular vesicles (EVs) promote tumorigenesis, premetastatic niche formation, and metastasis via their protein cargo. However, the proteins packaged by patient tumors into EVs cannot be determined in vivo because of the presence of EVs derived from other tissues. We therefore developed a cross-species proteomic method to quantify the human tumor-derived proteome of plasma EVs produced by patient-derived xenografts of four cancer types. Proteomic profiling revealed individualized packaging of novel protein cargo, and machine learning accurately classified the type of the underlying tumor.