Factors of Rowing Ergometer Performance in High-Level Female Rowers.

The present study investigated morphological and physiological factors of rowing ergometer performance over 2000 m (P2000, W) in 70 national and international level [27 lightweight (LW) and 43 heavyweight (HW)] female rowers. Maximal oxygen uptake (V̇O2max, L.min-1), maximal aerobic power (Pamax, W), power output corresponding to 4 mmol.L-1 blood lactate concentration expressed in absolute (PLa4, W) and relative to Pamax (PLa4%, %) values, peak power output (Ppeak, W), and rowing gross efficiency (RGE, %) were determined during an incremental rowing test. In the whole group, Ppeak was the best predictor of P2000 (r=0.89, p<0.001), as it was shown in men. PLa4 (r=0.87), V̇O2max (r=0.83), body mass (r=0.65), and height (r=0.64) were also significantly correlated with P2000 (p<0.001 for all). Ppeak was also the best predictor of P2000 when the two sub-groups LW and HW were considered separately. It was concluded that Ppeak is an overall index of physiological rowing capacity in groups of high-level LW and HW female rowers. The predictive value of Ppeak is similar to that of PLa4, but Ppeak presents the advantage of being obtained with a simple ergometer test without biological measurements.

Assessing species distribution using Google Street View: a pilot study with the Pine Processionary Moth.

Mapping species spatial distribution using spatial inference and prediction requires a lot of data. Occurrence data are generally not easily available from the literature and are very time-consuming to collect in the field. For that reason, we designed a survey to explore to which extent large-scale databases such as Google maps and Google Street View could be used to derive valid occurrence data. We worked with the Pine Processionary Moth (PPM) Thaumetopoea pityocampa because the larvae of that moth build silk nests that are easily visible. The presence of the species at one location can therefore be inferred from visual records derived from the panoramic views available from Google Street View. We designed a standardized procedure allowing evaluating the presence of the PPM on a sampling grid covering the landscape under study. The outputs were compared to field data. We investigated two landscapes using grids of different extent and mesh size. Data derived from Google Street View were highly similar to field data in the large-scale analysis based on a square grid with a mesh of 16 km (96% of matching records). Using a 2 km mesh size led to a strong divergence between field and Google-derived data (46% of matching records). We conclude that Google database might provide useful occurrence data for mapping the distribution of species which presence can be visually evaluated such as the PPM. However, the accuracy of the output strongly depends on the spatial scales considered and on the sampling grid used. Other factors such as the coverage of Google Street View network with regards to sampling grid size and the spatial distribution of host trees with regards to road network may also be determinant.

Is the expansion of the pine processionary moth, due to global warming, impacting the endangered Spanish moon moth through an induced change in food quality?

Recent climate change is known to affect the distribution of a number of insect species, resulting in a modification of their range boundaries. In newly colonized areas, novel interactions become apparent between expanding and endemic species sharing the same host. The pine processionary moth is a highly damaging pine defoliator, extending its range northwards and upwards in response to winter warming. Its expansion in the Alps has resulted in an invasion into the range of the Spanish moon moth, a red listed species developing on Scots pine. Pine processionary moth larvae develop during winter, preceding those of the moon moth, which hatch in late spring. Using pine trees planted in a clonal design, we experimentally tested the effect of previous winter defoliation by pine processionary moth larvae upon the survival and development of moon moth larvae. Feeding on foliage of heavily defoliated trees (>50%) resulted in a significant increase in the development time of moon moth larvae and a decrease in relative growth rate compared to feeding on foliage of undefoliated trees. Dry weight of pupae also decreased when larvae were fed with foliage of defoliated trees, and might, therefore, affect imago performances. However, lower defoliation degrees did not result in significant differences in larval performances compared to the control. Because a high degree of defoliation by pine processionary moth is to be expected during the colonization phase, its arrival in subalpine pine stands might affect the populations of the endangered moon moth.