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The Atlantic wolffish (Anarhichas lupus) was assessed as a species of Special Concern by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) under the Canadian Species-At-Risk Act (SARA) in 2001, and by the National Marine Fisheries Service in USA in 2004. Monitoring of marine Species-At-Risk would rely ideally on non-destructive methods. However, most monitoring of marine fish at-risk rely on trawl surveys that are potentially destructive of the environment. Inferring a species presence using environmental DNA (eDNA) detections offers an attractive alternative for Species-At-Risk monitoring, because it is non-destructive, specific, and sensitive. We developed and optimized a real-time quantitative PCR probe-based (qPCR) detection protocol that targets the eDNA of Atlantic wolffish, A. lupus. The qPCR protocol was validated in silico, in vitro, and in situ. Species-specificity was assessed in vitro by testing against the two other species of Anarhichas present in the northwest Atlantic. We did not observe DNA amplification for either of these two species. The assay was highly sensitive, with a limit of detection (95% confidence level) of 1.5 DNA copies per qPCR reaction. In situ tests showed that A. lupus eDNA is detected from expected depth strata in areas of known wolffish abundance. This study provides a proof-of-concept experiment that offers a robust, targeted, and non destructive protocol for detection eDNA of the Atlantic wolffish. [ABSTRACT FROM AUTHOR]

Meteorological ensemble members are a collection of scenarios for future weather issued by a meteorological center. Such ensembles nowadays form the main source of valuable information for probabilistic forecasting which aims at producing a predictive probability distribution of the quantity of interest instead of a single best guess point-wise estimate. Unfortunately, ensemble members cannot generally be considered as a sample from such a predictive probability distribution without a preliminary post-processing treatment to re-calibrate the ensemble. Two main families of post-processing methods, either competing such as the BMA or collaborative such as the EMOS, can be found in the literature. This paper proposes a mixed-effect model belonging to the collaborative family. The structure of the model is formally justified by Bruno de Finetti's representation theorem which shows how to construct operational statistical models of ensemble based on judgments of invariance under the relabeling of the members. Its interesting specificities are as follows: (1) exchangeability contributes to parsimony, with an interpretation of the latent pivot of the ensemble in terms of a statistical synthesis of the essential meteorological features of the ensemble members, (2) a multiensemble implementation is straightforward, allowing to take advantage of various information so as to increase the sharpness of the forecasting procedure. Focus is cast onto normal statistical structures, first with a direct application for temperatures, then with its very convenient Tobit extension for precipitation. Inference is performed by expectation maximization (EM) algorithms with both steps leading to explicit analytic expressions in the Gaussian temperature case, and recourse is made to stochastic conditional simulations in the zero-inflated precipitation case. After checking its good behavior on artificial data, the proposed post-processing technique is applied to temperature and precipitation ensemble forecasts produced for lead times from 1 to 9 days over five river basins managed by Hydro-Québec, which ranks among the world's largest electric companies. These ensemble forecasts, provided by three meteorological global forecast centers (Canadian, USA and European), were extracted from the THORPEX Interactive Grand Global Ensemble (TIGGE) database. The results indicate that post-processed ensembles are calibrated and generally sharper than the raw ensembles for the five watersheds under study. Supplementary materials accompanying this paper appear on-line. [ABSTRACT FROM AUTHOR]

Probabilistic forecasting aims at producing a predictive distribution of the quantity of interest instead of a single best guess point-wise estimate. With regard to water flow forecasts, the two main sources of uncertainty stem from unknown future rainfall and temperature (input error, i.e., meteorological uncertainty) and from the inadequacy of the deterministic simulator mimicking the rainfall-runoff (RR) transformation (hydrological uncertainty or RR error). These two sources of uncertainty can be dealt with separately and only the latter will be considered here. Only hydrological uncertainty is at stake when recorded meteorological data (instead of meteorological forecasts) are used as inputs to feed the RR simulator (RRS) for probabilistic predictions. The predictive performance of the RRS may strongly depend on the hydrological regimes: rapid flood variations induce large errors of anticipation but a series of dry events will translate into a much more smoother sequence of river levels due to the easily predictable behavior of the soil reservoir emptying. Consequently, a model with several regimes adapted to different error structures appears as a solution to cope with the issue of unstationary predictive variance. The river regime is modeled as a latent variable, the distribution of which is based on additional outputs of the RRS to be selected. Inference is performed by the EM algorithm with both steps leading to explicit analytic expressions. Asymptotic confidence regions for the estimates are provided within the same EM framework. Model selection is also performed, including the length of the model memory as well as the choice of explanatory variables for the latent regimes. The model is applied to a series of water flow forecasts routinely issued by two hydroelectricity producers in France and in Québec and compared with their present operational forecasting methods. [ABSTRACT FROM AUTHOR]

The partial synchronized spawning strategy adopted by some marine invertebrate broadcast-spawners can lead to the production of many distinct pools of larvae within a single reproductive cycle. Following the fate of these larval groups from birth to settlement with molecular markers might shed light on mechanisms regulating their population recruitment. Larvae and recruits of Mya arenaria, a partially spawning marine bivalve, were monitored and collected over 13 consecutive weeks during an entire reproductive cycle. Each sampled individual ( n = 218) was sorted according to size (early veligers, late veligers, post-larval recruits) and genotyped at seven microsatellite loci for comparisons among samples and with adult reference samples ( n = 270). While traditional differentiation statistics (e.g., pairwise ϴ, allelic richness) suggested the absence of sweepstakes reproductive success, the level of relatedness found within and among larvae and recruit samples suggested otherwise. Four samples out of ten were observed to have higher within-sample relatedness values than randomly expected, including the very first group of early veligers produced in the season (E1) and the last group of post-larvae who survived recruitment (P10). E1 individuals were also found to be more related than randomly expected to individuals of more than 80 % of all other samples including the last surviving recruits (P8 and P10). These results suggest that the first larvae produced in the season were the most successful to survive recruitment. Results also show direct evidence for larval retention and demonstrate for the first time larval and post-larval kin aggregation in a marine bivalve. [ABSTRACT FROM AUTHOR]