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A new insight into the Stygofauna Mundi: assembling a global dataset for aquatic fauna in subterranean environments
No full textThe potential of subterranean environments as models to address major evolutionary and ecological questions has been highlighted in the literature. They represent partially isolated, discrete units offering several replicates of the same evolutionary processes. Species occurrence data of these environments is abundant, although sparse in the literature or gathered in databases established according to regional, taxonomical, or ecological criteria. We here present a newly assembled dataset consisting of records of aquatic animals in all types of caves or wells from all over the world. Literature sources were gathered from Google Scholar by independently searching for each metazoan phylum/arthropod order, as well as the key words "cave", "groundwater", "well", or "stygobite", in English, Galician, Spanish, Portuguese, Catalonian, French, Italian, Hungarian, Greek, German, Polish, Russian, and Serbo-Croatian. The relevance of each source was confirmed after checking the title and the abstract. For each selected source, we examined its reference list in order to identify studies that were not published in journals indexed in the databases we searched. From the 6852 selected references, we manually extracted all records that concerned either 1. occurrence of a species in a given geographical area or 2. occurrence of any taxon in a particular cave or well. Records were classified as primary or secondary, depending on whether they provided new information or referred to already publish records, allowing us to identify redundant information in posterior analyses. Information for each access point was organized in as a gazetteer, including synonym names, geographical, ecological, and geological information. Following this strategy, we have obtained 48,800 records (32,769, primary) from 1957 references checked so far. Most records are amongst fish and crustaceans. In contrast, few data exist for other groups that are comparatively diverse outside caves, such as Nematoda. Relevant information will be included in World Register of Marine Cave Species (Fig. 1
Mechanisms regulating CO2 and CH4 dynamics in the Azorean volcanic lakes (Sao Miguel Island, Portugal)
No full textChemical and isotopic vertical profiles from the volcanic lakes of Sete Cidades, Santiago, Fogo, Congro and Furnas (Island of Sao Miguel, Azores Archipelago, Portugal) were studied to investigate the biogeochemical processes acting at different depths, with a focus on the CO2 and CH4 dynamics. These lakes are fed by meteoric water affected by seawater spray and interacting with volcanic rocks at a relatively low extent. In addition to volcanogenic gas inputs, the biogeochemical processes are influenced by microbial activities since the lakes offer specialized ecological niches for oxic and anoxic metabolism. The lakes were sampled in two extreme conditions of (partial) mixing (winter) and stratification (summer), respectively. The seasonal thermal stratification favored the development of anaerobic hypolimnia, showing relatively high concentrations of NH4+, NO3-, P and other minor species (Fe, Mn, Zn, As) controlled by microbial activity and minerogenetic processes occurring within the lake sediments. The strongly negative ?13C-TDIC values measured in almost all the studied lakes suggest dominant contribution of organic carbon. Dissolved gases were mostly consisting of atmospheric compounds with significant concentrations of CO2 and CH4. The ?13C-CO2 values were intermediate between those measured in the hydrothermal fluids and those typical of biogenic CO2. Dissolved CH4, which was the most abundant extra-atmospheric gas in the anoxic waters, was measured at significant concentrations even in the aerobic layers, especially in the winter season. This unexpected feature may tentatively be explained by admitting i) convective mixing of shallow and deep waters, and/or ii) aerobic CH4 production. Further investigations, focusing on the recognition of microbial populations able to produce CH4 at different redox conditions, may be useful to corroborate these intriguing hypotheses
Fisheries impacts on lake ecosystem structure in the context of a changing climate and trophic state
No full textThrough cascading effects within lake food webs, commercial and recreational fisheries may indirectly affect the abundances of organisms at lower trophic levels, such as phytoplankton, even if they are not directly consumed. So far, interactive effects of fisheries, changing trophic state and climate upon lake ecosystems have been largely overlooked. Here we analyse case studies from five European lake basins of differing trophic states (Lake V?rtsj?rv, two basins of Windermere, Lake Geneva and Lake Maggiore) with long-term limnological and fisheries data. Decreasing phosphorus concentrations (re-oligotrophication) and increasing water temperatures have been reported in all five lake basins, while phytoplankton concentration has decreased only slightly or even increased in some cases. To examine possible ecosystem-scale effects of fisheries, we analysed correlations between fish and fisheries data, and other food web components and environmental factors. Re-oligotrophication over different ranges of the trophic scale induced different fish responses. In the deeper lakes Geneva and Maggiore, we found a stronger link between phytoplankton and planktivorous fish and thus a more important cascading top-down effect than in other lakes. This connection makes careful ecosystem-based fisheries management extremely important for maintaining high water quality in such systems. We also demonstrated that increasing water temperature might favour piscivores at low phosphorus loading, but suppresses them at high phosphorus loading and might thus either enhance or diminish the cascading top-down control over phytoplankton with strong implications for water quality
Learning to spell in a language with transparent orthography: Distributional properties of orthography and whole-word lexical processing.
No full textWe examined how whole-word lexical information and knowledge of distributional properties of orthography interact in children\u27s spelling. High- versus low-frequency words, which included inconsistently spelled segments occurring more or less frequently in the orthography, were used in two experiments: (a) word spelling; (b) lexical priming of pseudoword spelling. Participants were 1st-, 2nd-, and 4th-grade Italian children. Word spelling showed sensitivity to the distributional properties of orthography in all children: accuracy in spelling uncommon transcription segments emerged progressively as a function of word frequency and schooling. Lexical priming effects emerged as a function of age. When related primes contained an uncommon segment, 2nd- and 4th-graders preferred uncommon segments than common ones in spelling target pseudowords, thus inverting the response trend found in the control condition. A smaller but significant effect was present in 1st- graders, who, unlike 2nd- and 4th-graders, still preferred common segments, only slightly increasing the use of uncommon ones. A larger priming effect emerged for high-frequency primes than low-frequency ones. Results indicate that children learning to spell in a transparent orthography are sensitive to the distributional properties of the orthography. However, whole-word lexical representations are also used, with larger effects in more skilled pupils
Survival in a little? Refugia of high-elevated plants in the Spanish Sierra Nevada
No full textClimate change is a serious threat to high-elevated plant species. There are three possible strategies to survive if they cannot exist in their inherent habitats any more: Upward shift, use of phenotypic plasticity ore movement to small-scaled local still suitable microhabitats. Furthermore, high-mountain plants are still exceptionally endangered since they are already at their ecologic limits. We analyzed future shift strategies based on possible climate scenarios considering current and future climate conditions. The study was conducted at the Spanish Sierra Nevada National Park as part of the ECOPOTENTIAL project
Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends
No full text1. Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). 2. We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. 3. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic- related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi- sensor/multi- platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. 4. This review encourages the use of SRS data in EMs for local applications, and underlines the necessity for a closer collaboration among EM developers and remote sensing scientists. With more upcoming satellite missions, especially the Sentinel platforms, concerted efforts to further integrate SRS into modelling are in great demand and these types of applications will certainly proliferate
Mapping coastal marine habitats and delineating the deep limits of the Neptune\u27s seagrass meadows using VHR earth observation data
No full textSeagrass meadows are one of the most important coastal habitats across the globe. These are mainly constituted by the marine plants of the genus Posidonia and Thalassia. In the Mediterranean Sea, Posidonia oceanica is the dominant endemic plant that affects physical, biogeochemical, and biological processes. The decline in the spatial distribution has been attributed to excessive anthropic pressures and other large-scale environmental changes. The monitoring of the spatial distribution requires an update and accurate seagrass meadows delineation, i.e. meadow edge marking with a replicable method. The present study aims to present an approach to support the coastal marine habitat mapping, under the scheme of the Natura 2000 network using very high resolution Earth observation data and to prove that satellite images can be used for the mapping of the deep limits of the seagrass meadows. Pixel-based classification and object-oriented image analysis have been implemented for the image classification. Pixel-based Support Vector Machines and object-based Nearest Neighbor classifiers provided the best results with an overall accuracy of more than 90%, while deep limits have been successfully identified and separated from the deep waters
Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry
No full textThe main objective of this research is to apply unmanned aerial system (UAS) data in synergy with field spectroradiometry for the accurate radiometric correction of Landsat-8 (L8) and Sentinel-2 (S2) imagery. The central hypothesis is that imagery acquired with multispectral UAS sensors that are well calibrated with highly accurate field measurements can fill in the scale gap between satellite imagery and conventional in situ measurements; this can be possible by sampling a larger area, including difficult-to-access land covers, in less time while simultaneously providing good radiometric quality. With this aim and by using near-coincident L8 and S2 imagery, we applied an upscaling workflow, whereby: (a) UAS-acquired multispectral data was empirically fitted to the reflectance of field measurements, with an extensive set of radiometric references distributed across the spectral domain; (b) drone data was resampled to satellite grids for comparison with the radiometrically corrected L8 and S2 official products (6S-LaSRC and Sen2Cor-SNAP, respectively) and the CorRad-MiraMon algorithm using pseudo-invariant areas, such as reflectance references (PIA-MiraMon), to examine their overall accuracy; (c) then, a subset of UAS data was used as reflectance references, in combination with the CorRad-MiraMon algorithm (UAS-MiraMon), to radiometrically correct the matching bands of UAS, L8, and S2; and (d) radiometrically corrected L8 and S2 scenes obtained with UAS-MiraMon were intercompared (intersensor coherence). In the first upscaling step, the results showed a good correlation between the field spectroradiometric measurements and the drone data in all evaluated bands (R2 > 0.946). In the second upscaling step, drone data indicated good agreement (estimated from root mean square error, RMSE) with the satellite official products in visible (VIS) bands (RMSEVIS < 2.484%), but yielded poor results in the near-infrared (NIR) band (RMSENIR > 6.688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS < 2.018%) than the other satellite radiometric correction methods in visible bands (6S-LaSRC (RMSE < 2.680%), Sen2Cor-SNAP (RMSE < 2.192%), and PIA-MiraMon (RMSE < 3.130%), but did not achieve sufficient results in the NIR band (RMSENIR < 7.530%); this also occurred with all other methods. In the intercomparison step, the UAS-MiraMon method achieved an excellent intersensor (L8-S2) coherence (RMSEVIS < 1%). The UAS-sampled area involved 51 L8 (30 m) pixels, 143 S2 (20 m) pixels, and 517 S2 (10 m) pixels. The drone time needed to cover this area was only 10 min, including areas that were difficult to access. The systematic sampling of the study area was achieved with a pixel size of 6 cm, and the raster nature of the sampling allowed for an easy but rigorous resampling of UAS data to the different satellite grids. These advances improve human capacities for conventional field spectroradiometry samplings. However, our study also shows that field spectroradiometry is the backbone that supports the full upscaling workflow. In conclusion, the synergy between field spectroradiometry, UAS sensors, and Landsat-like satellite data can be a useful tool for accurate radiometric corrections used in local environmental studies or the monitoring of protected areas around the world
Processing Compounds: What Frequency (Alone) Cannot Explain
No full textObserved elevation in typing latency for the initial letter of the second constituent of an English compound, compared with the typing time of the final letter of the first constituent (Gagn? & Spalding 2016), suggests that both compounds ( snowball ) and pseudo-compounds ( carpet ) are decomposed but also that full form representations are available in the lexical store. To gain further insight into the lexical representations underlying typing, we used computational modelling. In particular, we used superpositional models of word memory, based on Self-Organising Recurrent Maps (TSOMs) (Ferro et al. 2016; Marzi et al. 2016), where both simple and compound words are processed (and stored) using the same pool of processing (and memory) resources, to model the elevation in typing time at the constituent boundary and the rate of typing. In addition, we also considered models based in the Compositional Distributional Semantics framework (CAOSS, Marelli et al. 2017), to simulate independent effects of semantic transparency on compound typing (Gagn? & Spalding 2016). Due to co-activation and competition between compounds and their constituent words in TSOMs, levels of activation of processing nodes per letter positions appear to reflect degrees of context-sensitive predictability: the higher the level, the more expected the letter in that position. In English compounds, activation levels appeared to exhibit a characteristically U-shaped pattern, with min values centred on the constituent boundary. A similar pattern was found for pseudo-compounds, which nonetheless present a less pronounced U-shaped pattern and a higher activation value at the morpheme boundary than compounds do. The difference is in line with the higher speed-up rate in typing pseudo-compounds than compounds reported in Gagn? and Spalding (2016). TSOMs were trained on letter-based representations, so computer experiments could simulate peripheral effects of serial processing of compound structure before lexical access. To investigate post-lexical issues, we also tested computational models of generation of the meanings of novel compounds based on CAOSS, which proved to be able to account for well-established relational effects in compound processing (Gagn? 2001; Gagn? & Shoben 1997) with an unsupervised data-driven framework (Marelli et al. 2017). We ran a mixed-effects regression analysis of the data in Gagn? and Spalding (2016) using vector-semantics estimates and TSOM activation levels to predict typing time for the initial letter of the second constituent. There was a negative effect of TSOM letter activation levels: i.e. the more active a letter node is, the faster a subject is at typing the letter ( t =-2.7 p =.007). Also, there was a positive effect of CAOSS-based compositionality estimates: i.e. the more easily a compound\u27s lexicalized meaning can be obtained through compositional operations on single constituent vectors, the slower participants were at typing the first letter of the second constituent ( t =2.4, p =.017). These results have interesting implications for an integrative computational architecture accounting for the whole range of experimental evidence reported by Gagn? and Spalding (2016). In particular we will focus on evidence of a stronger competition (and longer typing time) in Transparent-Transparent and Transparent-Opaque compounds, vs. Opaque-Transparent compounds, which gives an indication of a non-trivial interaction between semantic compositionality and serial processing effects
Comparison of global and continental land cover products for selected study areas in south central and eastern European region
No full textLand cover is one of the key terrestrial variables used for monitoring and as input for modelling in support of achieving the United Nations Strategical Development Goals. Global and Continental Land Cover Products (GCLCs) aim to provide the required harmonized information background across areas; thus, they are not being limited by national or other administrative nomenclature boundaries and their production approaches. Moreover, their increased spatial resolution, and consequently their local relevance, is of high importance for users at a local scale. During the last decade, several GCLCs were developed, including the Global Historical Land-Cover Change Land-Use Conversions (GLC), the Globeland-30 (GLOB), Corine-2012 (CLC) and GMES/ Copernicus Initial Operation High Resolution Layers (GIOS). Accuracy assessment is of high importance for product credibility towards incorporation into decision chains and implementation procedures, especially at local scales. The present study builds on the collaboration of scientists participating in the Global Observations of Forest Cover-Global Observations of Land Cover Dynamics (GOFC-GOLD), South Central and Eastern European Regional Information Network (SCERIN). The main objective is to quantitatively evaluate the accuracy of commonly used GCLCs at selected representative study areas in the SCERIN geographic area, which is characterized by extreme diversity of landscapes and environmental conditions, heavily affected by anthropogenic impacts with similar major socio-economic drivers. The employed validation strategy for evaluating and comparing the different products is detailed, representative results for the selected areas from nine SCERIN countries are presented, the specific regional differences are identified and their underlying causes are discussed. In general, the four GCLCs products achieved relatively high overall accuracy rates: 74-98% for GLC (mean: 93.8%), 79-92% for GLOB (mean: 90.6%), 74-91% for CLC (mean: 89%) and 72-98% for GIOS (mean: 91.6%), for all selected areas. In most cases, the CLC product has the lower scores, while the GLC has the highest, closely followed by GIOS and GLOB. The study revealed overall high credibility and validity of the GCLCs products at local scale, a result, which shows expected benefit even for local/regional applications. Identified class dependent specificities in different landscape types can guide the local users for their reasonable usage in local studies. Valuable information is generated for advancing the goals of the international GOFC-GOLD program and aligns well with the agenda of the NASA Land-Cover/Land-Use Change Program to improve the quality and consistency of space-derived higher-level products