Sympatric Dreissena species in the Meuse River : towards a dominance shift from zebra to quagga mussels

The rapid spread of the quagga mussel, Dreissena rostriformis, in Western Europe is of particular concern since the species is known to have serious ecological and economic impacts, similar to those of the well-established zebra mussel, Dreissena polymorpha. This study aimed (1) to provide an update on the quagga mussel distribution in several Belgian inland waterways, and (2) to check if a shift in dominance between Dreissena species is occurring. Using density measurements and artificial substrate samplers, we compared population dynamics for both species at different time-points based on size-frequency distribution. Our results show that quagga mussels are spreading rapidly throughout Belgium via a number of possible invasion fronts based around large rivers and canals. The quagga mussel became the dominant dreissenid species in both the Meuse River and a number of Belgian canals. In just three years, quagga mussel’s relative abundance increased from 2.9% (±2.9) to 52.6% (±43.1) of the total dreissenid population in the Meuse River. The most rapid increase in abundance has occurred in the Albert Canal, where quagga mussels achieved a mean relative abundance of 80% two years after the first observation. In the Meuse River, the quagga mussel displays a faster growth rate and/or earlier reproduction than the zebra mussel. We discuss different mechanisms that could explain the quagga mussel’s apparent competitive advantage over the zebra mussel

First records of Dreissena rostriformis bugensis (Andrusov 1897) in the Meuse River

info:eu-repo/semantics/publishe

Phylogeny and androgenesis in the invasive Corbicula clams (Bivalvia, Corbiculidae) in Western Europe

info:eu-repo/semantics/publishe

SARS-CoV-2 Surveillance in Belgian Wastewaters

Wastewater-based surveillance was conducted by the national public health authority to monitor SARS-CoV-2 circulation in the Belgian population. Over 5 million inhabitants representing 45% of the Belgian population were monitored throughout 42 wastewater treatment plants for 15 months comprising three major virus waves. During the entire period, a high correlation was observed between the daily new COVID-19 cases and the SARS-CoV-2 concentration in wastewater corrected for rain impact and covered population size. Three alerting indicators were included in the weekly epidemiological assessment: High Circulation, Fast Increase, and Increasing Trend. These indicators were computed on normalized concentrations per individual treatment plant to allow for a comparison with a reference period as well as between analyses performed by distinct laboratories. When the indicators were not corrected for rain impact, rainy events caused an underestimation of the indicators. Despite this negative impact, the indicators permitted us to effectively monitor the evolution of the fourth virus wave and were considered complementary and valuable information to conventional epidemiological indicators in the weekly wastewater reports communicated to the National Risk Assessment&nbsp;Group.</p

Biometric conversion factors as a unifying platform for comparative assessment of invasive freshwater bivalves

Invasive bivalves continue to spread and negatively impact freshwater ecosystems worldwide. As different metrics for body size and biomass are frequently used within the literature to standardise bivalve-related ecological impacts (e.g. respiration and filtration rates), the lack of broadly applicable conversion equations currently hinders reliable comparison across bivalve populations. To facilitate improved comparative assessment among studies originating from disparate geographical locations, we report body size and biomass conversion equations for six invasive freshwater bivalves (or species complex members) worldwide: Corbicula fluminea, C. largillierti, Dreissena bugensis, D. polymorpha, Limnoperna fortunei and Sinanodonta woodiana, and tested the reliability (i.e. precision and accuracy) of these equations. Body size (length, width and height) and biomass metrics of living-weight (LW), wet-weight (WW), dry-weight (DW), dry shell-weight (SW), shell free dry-weight (SFDW) and ash-free dry-weight (AFDW) were collected from a total of 44 bivalve populations located in Asia, the Americas and Europe. Relationships between body size and individual biomass metrics, as well as proportional weight-to-weight conversion factors, were determined. For most species, although inherent variation existed between sampled populations, body size directional measurements were found to be good predictors of all biomass metrics (e.g. length to LW, WW, SW or DW: R2 = 0.82–0.96), with moderate to high accuracy for mean absolute error (MAE): ±9.14%–24.19%. Similarly, narrow 95% confidence limits and low MAE were observed for most proportional biomass relationships, indicating high reliability for the calculated conversion factors (e.g. LW to AFDW; CI range: 0.7–2.0, MAE: ±0.7%–2.0%). Synthesis and applications. Our derived biomass prediction equations can be used to rapidly estimate the biologically active biomass of the assessed species, based on simpler biomass or body size measurements for a wide range of situations globally. This allows for the calculation of approximate average indicators that, when combined with density data, can be used to estimate biomass per geographical unit-area and contribute to quantification of population-level effects. These general equations will support meta-analyses, and allow for comparative assessment of historic and contemporary data. Overall, these equations will enable conservation managers to better understand and predict ecological impacts of these bivalves.Fil: Coughlan, Neil E.. The Queens University of Belfast; Irlanda. University College Cork; IrlandaFil: Cunningham, Eoghan M.. The Queens University of Belfast; IrlandaFil: Cuthbert, Ross N.. The Queens University of Belfast; Irlanda. Geomar-Helmholtz Centre for Ocean Research Kiel; AlemaniaFil: Joyce, Patrick W. S.. The Queens University of Belfast; IrlandaFil: Anastácio, Pedro. Universidade de Évora; PortugalFil: Banha, Filipe. Universidade de Évora; PortugalFil: Bonel, Nicolás. Université Montpellier II; Francia. Centre National de la Recherche Scientifique; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Bradbeer, Stephanie J.. University of Leeds; Reino UnidoFil: Briski, Elizabeta. Geomar-Helmholtz Centre for Ocean Research Kiel; AlemaniaFil: Butitta, Vince L.. University of Wisconsin; Estados UnidosFil: Cadková, Zuzana. Czech University of Life Sciences; República ChecaFil: Dick, Jaimie T. A.. The Queens University of Belfast; IrlandaFil: Douda, Karel. Czech University of Life Sciences; República ChecaFil: Eagling, Lawrence E.. The Queens University of Belfast; IrlandaFil: Ferreira Rodríguez, Noé. Universidad de Vigo; EspañaFil: Hünicken, Leandro Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; ArgentinaFil: Johansson, Mattias L.. University of North Georgia; Estados UnidosFil: Kregting, Louise. The Queens University of Belfast; IrlandaFil: Labecka, Anna Maria. Jagiellonian University; PoloniaFil: Li, Deliang. Hunan Agricultural University; ChinaFil: Liquin, Florencia Fernanda. Universidad Nacional de Salta. Facultad de Ciencias Naturales. Instituto para el Estudio de la Biodiversidad de Invertebrados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta; ArgentinaFil: Marescaux, Jonathan. University of Namur; Bélgica. e-biom; BélgicaFil: Morris, Todd J.. Fisheries and Ocean Canada; CanadáFil: Nowakowska, Patrycja. University of Gdansk; PoloniaFil: Ozgo, Malgorzata. Kazimierz Wielki University; PoloniaFil: Paolucci, Esteban Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; ArgentinaFil: Peribáñez, Miguel A.. Universidad de Zaragoza; EspañaFil: Riccardi, Nicoletta. Consiglio Nazionale delle Ricerche; ItaliaFil: Smith, Emily R. C.. University College London; Estados UnidosFil: Sylvester, Francisco. Universidad Nacional de Salta. Facultad de Ciencias Naturales. Instituto para el Estudio de la Biodiversidad de Invertebrados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta; Argentin

Genetic structure of fragmented southern populations of African Cape buffalo (Syncerus caffer caffer)

peer reviewedBackground African wildlife experienced a reduction in population size and geographical distribution over the last millennium, particularly since the 19th century as a result of human demographic expansion, wildlife overexploitation, habitat degradation and cattle-borne diseases. In many areas, ungulate populations are now largely confined within a network of loosely connected protected areas. These metapopulations face gene flow restriction and run the risk of genetic diversity erosion. In this context, we assessed the “genetic health” of free ranging southern African Cape buffalo populations (S.c. caffer) and investigated the origins of their current genetic structure. The analyses were based on 264 samples from 6 southern African countries that were genotyped for 14 autosomal and 3 Y-chromosomal microsatellites. Results The analyses differentiated three significant genetic clusters, hereafter referred to as Northern (N), Central (C) and Southern (S) clusters. The results suggest that splitting of the N and C clusters occurred around 6000 to 8400 years ago. Both N and C clusters displayed high genetic diversity (mean allelic richness (Ar) of 7.217, average genetic diversity over loci of 0.594, mean private alleles (Pa) of 11), low differentiation, and an absence of an inbreeding depression signal (mean FIS = 0.037). The third (S) cluster, a tiny population enclosed within a small isolated protected area, likely originated from a more recent isolation and experienced genetic drift (FIS = 0.062, mean Ar = 6.160, Pa = 2). This study also highlighted the impact of translocations between clusters on the genetic structure of several African buffalo populations. Lower differentiation estimates were observed between C and N sampling localities that experienced translocation over the last century. Conclusions We showed that the current genetic structure of southern African Cape buffalo populations results from both ancient and recent processes. The splitting time of N and C clusters suggests that the current pattern results from human-induced factors and/or from the aridification process that occurred during the Holocene period. The more recent S cluster genetic drift probably results of processes that occurred over the last centuries (habitat fragmentation, diseases). Management practices of African buffalo populations should consider the micro-evolutionary changes highlighted in the present study