Strengthening the link between climate, hydrological and species distribution modeling to assess the impacts of climate change on freshwater biodiversity.

To understand the resilience of aquatic ecosystems to environmental change, it is important to determine how multiple, related environmental factors, such as near-surface air temperature and river flow, will change during the next century. This study develops a novel methodology that combines statistical downscaling and fish species distribution modeling, to enhance the understanding of how global climate changes (modeled by global climate models at coarse-resolution) may affect local riverine fish diversity. The novelty of this work is the downscaling framework developed to provide suitable future projections of fish habitat descriptors, focusing particularly on the hydrology which has been rarely considered in previous studies. The proposed modeling framework was developed and tested in a major European system, the Adour-Garonne river basin (SW France, 116,000 km(2)), which covers distinct hydrological and thermal regions from the Pyrenees to the Atlantic coast. The simulations suggest that, by 2100, the mean annual stream flow is projected to decrease by approximately 15% and temperature to increase by approximately 1.2 °C, on average. As consequence, the majority of cool- and warm-water fish species is projected to expand their geographical range within the basin while the few cold-water species will experience a reduction in their distribution. The limitations and potential benefits of the proposed modeling approach are discussed.

Long-term changes in water physicochemistry in the Adour-Garonne hydrographic network during the last three decades.

This study details a trend analysis covering a 30-year period (1975-2004), for 19 physicochemical parameters at 45 surface water sites in the Adour-Garonne basin, south-west France. To perform statistical analysis, we used the annual average of each variable. The analysis revealed sites affected by strong patterns of temporal variation and sites with weak or imperceptible changes of water quality. More than half the studied sites were affected by chemical changes. Trends were generally clearest in the River Garonne continuum, but similar tendencies could also be identified in tributaries. The overall trends indicated the onset of an increase of water temperature starting about 20 years ago and partial recovery from eutrophication during the last decade. As expected, the strongest trend affected the temperature regime of the hydrosystems, there being a more significant warming during the second decade of the study (1984-1994). Additionally, at many sites nutrient loads were lower between 1995 and 2004. This confirms a downward trend in eutrophication status resulting from more stringent control of sewage treatment despite the constant increase of anthropic pressure. Sites that did not present any trends are extreme sites located at each end of the river gradient: headwater and downstream sites under tidal influence. Other sites not affected by changes are those strongly perturbed by human activities showing a high level of degradation.

ISSR-PCR: tool for discrimination and genetic structure analysis of Plutella xylostella populations native to different geographical areas.

The diamondback moth (DBM), Plutella xylostella (L.) is considered as the most destructive pest of Brassicaceae crops world-wide. Its migratory capacities and development of insecticide resistance in many populations leads to more difficulties for population management. To control movement of populations and apparitions of resistance carried by resistant migrant individuals, populations must be identified using genetic markers. Here, seven different ISSR markers have been tested as a tool for population discrimination and genetic variations among 19 DBM populations from Canada, USA, Brazil, Martinique Island, France, Romania, Austria, Uzbekistan, Egypt, Benin, South Africa, Réunion Island, Hong Kong, Laos, Japan and four localities in Australia were assessed. Two classification methods were tested and compared: a common method of genetic distance analyses and a novel method based on an advanced statistical method of the Artificial Neural Networks' family, the Self-Organizing Map (SOM). The 188 loci selected revealed a very high variability between populations with a total polymorphism of 100% and a global coefficient of gene differentiation estimated by the Nei's index (Gst) of 0.238. Nevertheless, the largest part of variability was expressed among individuals within populations (AMOVA: 73.71% and mean polymorphism of 94% within populations). Genetic differentiation among the DBM populations did not reflect geographical distances between them. The two classification methods have given excellent results with less than 1.3% of misclassified individuals. The origin of the high genetic differentiation and efficiency of the two classification methods are discussed.