A validated protocol for eDNA-based monitoring of within-species genetic diversity in a pond-breeding amphibian

In light of the dramatic decline in amphibian biodiversity, new cost-efficient tools to rapidly monitor species abundance and population genetic diversity in space and time are urgently needed. It has been amply demonstrated that the use of environmental DNA (eDNA) for single-species detection and characterization of community composition can increase the precision of amphibian monitoring compared to traditional (observational) approaches. However, it has been suggested that the efficiency and accuracy of the eDNA approach could be further improved by more timely sampling; in addition, the quality of genetic diversity data derived from the same DNA has been confirmed in other vertebrate taxa, but not amphibians. Given the availability of previous tissue-based genetic data, here we use the common frog Rana temporaria Linnaeus, 1758 as our target species and an improved eDNA protocol to: (i) investigate differences in species detection between three developmental stages in various freshwater environments; and (ii) study the diversity of mitochondrial DNA (mtDNA) haplotypes detected in eDNA (water) samples, by amplifying a specific fragment of the COI gene (331 base pairs, bp) commonly used as a barcode. Our protocol proved to be a reliable tool for monitoring population genetic diversity of this species, and could be a valuable addition to amphibian conservation and wetland management

Correction: Endrizzi et al. Relationships between Intensity and Liking for Chemosensory Stimuli in Food Models: A Large-Scale Consumer Segmentation (Foods 2022, 11, 5)

This study, which was conducted as part of the Italian Taste project, was aimed at exploring the relationship between actual liking and sensory perception in four food models. Each food model was spiked with four levels of prototypical tastant (i.e., citric acid, sucrose, sodium chloride, capsaicin) to elicit a target sensation (TS) at an increasing perceived intensity. Participants (N = 2258; 59% women, aged 18–60) provided demographic information, a stated liking for 40 different foods/beverages, and their responsiveness to tastants in water. A food-specific Pearson’s coefficient was calculated individually to estimate the relationship between actual liking and TS responsiveness. Considering the relationship magnitude, consumers were grouped into four food-specific clusters, depending on whether they showed a strong negative (SNC), a weak negative (WNC), a weak positive (WPC), or a strong positive correlation (SPC). Overall, the degree of liking raised in parallel with sweetness responsiveness, fell as sourness and pungency perception increased, and showed an inverted U-shape relationship with saltiness. The SNC clusters generally perceived TSs at higher intensities, except for sourness. Clusters were validated by associating the level of stated liking towards food/beverages; however, some unexpected indications emerged: adding sugar to coffee or preferring spicy foods differentiated those presenting positive correlations from those showing negative correlations. Our findings constitute a step towards a more comprehensive understanding of food preference

Snow simulation and forecasting through all Norway: the SeNorge model

A model has been recently set up (since 2006) that provides the snow conditions, along with other hydrometeorological parameters, for all Norway at 1 km resolution. This model is called “Senorge” (meaning “See Norway”), and is described in this work. It is an operative model that is run daily predicting snow water equivalent, snow depth, snow changes (for melting and snowfalls), and snow wetness, and the results are published daily in the Senorge website (www.senorge.no). Though melting is estimated with a temperature index approach, a simple module describing snow metamorphism has been implemented and tested against several measurements performed in snow pillows. The goals and ambitions are various, and range from water resources monitoring and water availability assessment for hydroelectric power to avalanche warning and evaluation of skiing condition. Efforts are currently being made to improve this model, in particular implementing a full energy balance model and trying some procedures of data assimilation of remotely sensed data. In addition, a progress in the snowpack description in order to provide information about avalanche occurrences is being investigate

Investigating the effects of lateral water flow on the spatial patterns of thaw depth

The effects of lateral water flow on the spatial distribution of the thaw depth in permafrost terrain have rarely been investigated with models. The GEOtop model, which solves the soil energy and water budgets in a coupled way and accounts for phase change, has been used to better understand how soil moisture spatial differences in the unfrozen upper part of the ground affect the thawing soil energy balance in idealized hillslope topography. Results show that, in terrains with thermal conductivity highly variable with soil moisture such as organic soils, wetter areas exhibit deeper thaw than drier areas. Conversely, if thermal conductivity depends less on soil moisture, as in mineral soils, the result is the opposite since the effect of the higher thermal capacity resulting from higher soil moisture prevails

Sensitivities and uncertainties of modeled ground temperatures in mountain environments

Model evaluation is often performed at few locations due to the lack of spatially distributed data. Since the quantification of model sensitivities and uncertainties can be performed independently from ground truth measurements, these analyses are suitable to test the influence of environmental variability on model evaluation. In this study, the sensitivities and uncertainties of a physically based mountain permafrost model are quantified within an artificial topography. The setting consists of different elevations and exposures combined with six ground types characterized by porosity and hydraulic properties. The analyses are performed for a combination of all factors, that allows for quantification of the variability of model sensitivities and uncertainties within a whole modeling domain. <br><br> We found that model sensitivities and uncertainties vary strongly depending on different input factors such as topography or different soil types. The analysis shows that model evaluation performed at single locations may not be representative for the whole modeling domain. For example, the sensitivity of modeled mean annual ground temperature to ground albedo ranges between 0.5 and 4 °C depending on elevation, aspect and the ground type. South-exposed inclined locations are more sensitive to changes in ground albedo than north-exposed slopes since they receive more solar radiation. The sensitivity to ground albedo increases with decreasing elevation due to shorter duration of the snow cover. The sensitivity in the hydraulic properties changes considerably for different ground types: rock or clay, for instance, are not sensitive to uncertainties in the hydraulic properties, while for gravel or peat, accurate estimates of the hydraulic properties significantly improve modeled ground temperatures. The discretization of ground, snow and time have an impact on modeled mean annual ground temperature (MAGT) that cannot be neglected (more than 1 °C for several discretization parameters). We show that the temporal resolution should be at least 1 h to ensure errors less than 0.2 °C in modeled MAGT, and the uppermost ground layer should at most be 20 mm thick. <br><br> Within the topographic setting, the total parametric output uncertainties expressed as the length of the 95% uncertainty interval of the Monte Carlo simulations range from 0.5 to 1.5 °C for clay and silt, and ranges from 0.5 to around 2.4 °C for peat, sand, gravel and rock. These uncertainties are comparable to the variability of ground surface temperatures measured within 10 m × 10 m grids in Switzerland. The increased uncertainties for sand, peat and gravel are largely due to their sensitivity to the hydraulic conductivity

Soil moisture estimation by assimilating L-band microwave brightness temperature with geostatistics and observation localization.

The observation could be used to reduce the model uncertainties with data assimilation. If the observation cannot cover the whole model area due to spatial availability or instrument ability, how to do data assimilation at locations not covered by observation? Two commonly used strategies were firstly described: One is covariance localization (CL); the other is observation localization (OL). Compared with CL, OL is easy to parallelize and more efficient for large-scale analysis. This paper evaluated OL in soil moisture profile characterizations, in which the geostatistical semivariogram was used to fit the spatial correlated characteristics of synthetic L-Band microwave brightness temperature measurement. The fitted semivariogram model and the local ensemble transform Kalman filter algorithm are combined together to weight and assimilate the observations within a local region surrounding the grid cell of land surface model to be analyzed. Six scenarios were compared: 1_Obs with one nearest observation assimilated, 5_Obs with no more than five nearest local observations assimilated, and 9_Obs with no more than nine nearest local observations assimilated. The scenarios with no more than 16, 25, and 36 local observations were also compared. From the results we can conclude that more local observations involved in assimilation will improve estimations with an upper bound of 9 observations in this case. This study demonstrates the potentials of geostatistical correlation representation in OL to improve data assimilation of catchment scale soil moisture using synthetic L-band microwave brightness temperature, which cannot cover the study area fully in space due to vegetation effects