Evaluating the performance of sampling plans for phycotoxins in shellfish: Improvement of an existing method

According to the EU Regulation 854/2004, sampling plans must be set up to monitor production areas for the level of okadaic acid (OA) equivalents in live mussel. The level of these toxins, which are produced by harmful algal blooms, must not exceed 160 μg/kg of raw meat (Regulation 853/2004/EC). A sampling plan assessment consists in obtaining an OC (Operating Characteristic) curve showing both consumer and producer risks. The first risk is the risk of opening a shellfish area for harvest while the contamination level is above the threshold; whereas the second risk is the risk of closing a shellfish area having a contamination level under the threshold.For sampling plan validation purposes, a classical mathematical method was improved for the prediction of variance as function of the mean contamination level thanks to prior knowledge of the theoretical distribution fitting the observed OA levels among individual mussels. Indeed, knowing that, thanks to a regression analysis of literature data, for the lognormal distribution the scale parameter was observed to be directly proportional to the location parameter, the regression bias could be lowered. Literature data from Norway and Sweden showed different levels of variability between contamination events and depuration. However, the highest variability level was chosen to propose a best fit sampling plan in order to have a better approach of reality. It consisted of taking two samples of 50 mussels (Mytilus sp.) for this geographic location (Norway and Sweden).     

A method to measure predictive ability of an injury risk curve using an observation-adjusted area under the receiver operating characteristic curve

Area under the receiver operating characteristic curve (AROC) is commonly used to choose a biomechanical metric from which to construct an injury risk curve (IRC). However, AROC may not handle censored datasets adequately. Survival analysis creates robust estimates of IRCs which accommodate censored data. We present an observation-adjusted ROC (oaROC) which uses the survival-based IRC to estimate the AROC. We verified and evaluated this method using simulated datasets of different censoring statuses and sample sizes. For a dataset with 1000 left and right censored observations, the median AROC closely approached the oaROC_True, or the oaROC calculated using an assumed “true” IRC, differing by a fraction of a percent, 0.1%. Using simulated datasets with various censoring, we found that oaROC converged onto oaROC_True in all cases. For datasets with right and non-censored observations, AROC did not converge onto oaROC_True. oaROC for datasets with only non-censored observations converged the fastest, and for a dataset with 10 observations, the median oaROC differed from oaROC_True by 2.74% while the corresponding median AROC with left and right censored data differed from oaROC_True by 9.74%. We also calculated the AROC and oaROC for a published side impact dataset, and differences between the two methods ranged between −24.08% and 24.55% depending on metric. Overall, when compared with AROC, we found oaROC performs equivalently for doubly censored data, better for non-censored data, and can accommodate more types of data than AROC. While more validation is needed, the results indicate that oaROC is a viable alternative which can be incorporated into the metric selection process for IRCs.     

Phytotoxicity of tin mine waste and accumulation of involved heavy metals in common buckwheat (Fagopyrum esculentum Moench)

Extraction and processing of cassiterite (SnO₂) left large tailings with high concentrations of tin, tungsten, molybdenum and lithium. Information on the phytotoxicity of mine waste is important with regard to ecological hazards. Exposure studies help to identify plants useful for the stabilization of waste tips and the phytomining of metals. A greenhouse study was performed using a dilution series of mine waste and four crops, a halophytic and a metallophytic species to derive dose response curves. Based on effective doses for growth reductions, sensitivity increased in the following order: maize > common buckwheat > quinoa > garden bean. Element analyses in different species and compartments of common buckwheat grown in a mixture of standard soil and 25% of the mine waste showed that only low levels of the metals were taken up and that transfer to seed tissues was negligible. As indicated by soil metal levels prior to and after the experiment, only lithium and arsenic proved to be plant available and reached high levels in green tissues while seed levels were low. The experiment confirmed differences in the uptake of metals with regard to elements and species. Common buckwheat is a suited candidate for cultivation on metal polluted soils.     

An approach based on the total‐species accumulation curve and higher taxon richness to estimate realistic upper limits in regional species richness

Most of accumulation curves tend to underestimate species richness, as they do not consider spatial heterogeneity in species distribution, or are structured to provide lower bound estimates and limited extrapolations. The total‐species (T–S) curve allows extrapolations over large areas while taking into account spatial heterogeneity, making this estimator more prone to attempt upper bound estimates of regional species richness. However, the T–S curve may overestimate species richness due to (1) the mismatch among the spatial units used in the accumulation model and the actual units of variation in β‐diversity across the region, (2) small‐scale patchiness, and/or (3) patterns of rarity of species. We propose a new framework allowing the T–S curve to limit overestimation and give an application to a large dataset of marine mollusks spanning over 11 km² of subtidal bottom (W Mediterranean). As accumulation patterns are closely related across the taxonomic hierarchy up to family level, improvements of the T–S curve leading to more realistic estimates of family richness, that is, not exceeding the maximum number of known families potentially present in the area, can be considered as conducive to more realistic estimates of species richness. Results on real data showed that improvements of the T–S curve to accounts for true variations in β‐diversity within the sampled areas, small‐scale patchiness, and rarity of families led to the most plausible richness when all aspects were considered in the model. Data on simulated communities indicated that in the presence of high heterogeneity, and when the proportion of rare species was not excessive (>2/3), the procedure led to almost unbiased estimates. Our findings highlighted the central role of variations in β‐diversity within the region when attempting to estimate species richness, providing a general framework exploiting the properties of the T–S curve and known family richness to estimate plausible upper bounds in γ‐diversity.     

Habitat preferences of European grayling in a medium size stream, the Ain river, France

The three grayling Thymallus thymallus age-classes had strong preferences for their local habitat in the Ain river. All age-classes preferred high water velocities between 70 and 110 cm s^-1, confirming that grayling is a typical rheophilic species. An important intraspecific segregation regarding depth occurred, as large individuals preferred deeper water than small ones (optimal ranges=50–60, 80–120 and 100–140 cm for 0+, 1+ and adults respectively). The three age-classes had similar preferences for small substratum articles, with optimal values between 0·5 and 16·0 mm. Preference curves were generally comparable between the two sites, despite important differences in habitat availability. It is emphasized that grayling needs various habitat conditions to achieve its entire life cycle. The increasing scarcity of this species underlines the urgency of protecting lotic habitat integrity and diversity.     

Spatial and temporal variations in species occurrence rate affect the accuracy of occurrence models

Aim Predictive models of species occurrence have potential for prioritizing areas for competing land uses. Before widespread application, however, it is necessary to evaluate performance using independent data and effective accuracy measures. The objectives of this study were to (1) compare the effects of species occurrence rate on model accuracy, (2) assess the effects of spatial and temporal variation in occurrence rate on model accuracy, and (3) determine if the number of predictor variables affected model accuracy. Location We predicted the distributions of breeding birds in three adjacent mountain ranges in the Great Basin (Nevada, USA). Methods For each of 18 species, we developed separate models using five different data sets — one set for each of 2 years (to address the effects of temporal variation), and one set for each of three possible pairs of mountain ranges (to address the effects of spatial variation). We evaluated each model with an independent data set using four accuracy measures: discrimination ability [area under a receiver operating characteristic curve (AUC)], correct classification rate (CCR), proportion of presences correctly classified (sensitivity), and proportion of absences correctly classified (specificity). Results Discrimination ability was not affected by occurrence rate, whereas the other three accuracy measures were significantly affected. CCR, sensitivity and specificity were affected by species occurrence rate in the evaluation data sets to a greater extent than in the model‐building data sets. Discrimination ability was the only accuracy measure affected by the number of variables in a model. Main conclusions Temporal variation in species occurrence appeared to have a greater impact than did spatial variation. When temporal variation in species distributions is great, the relative costs of omission and commission errors should be assessed and long‐term census data should be examined before using predictive models of occurrence in a management setting.     

The suitability of the degradation gradient method in arid Namibia

The Degradation Gradient Method (DGM) is a sophisticated technique for the assessment of range condition. It applies multivariate analyses of herbaceous species data to detect subtle degrees of overgrazing. The suitability of this multivariate method was tested in the central Highland Savanna of Namibia by comparing its results against a univariate analysis of herbaceous data in a simple but robust Range‐Unit Model. Despite aridity and topographical heterogeneity, the DGM performed unexpectedly well under these conditions. The relative instability of this dry savanna system favoured the applicability of the DGM by promoting a clear grazing gradient. Using species density data only resulted in an incorrect outcome of the multivariate analysis. The sensitivity of the DGM could be improved by combining density and cover data.