Search results for "e learning"
showing 10 items of 2703 documents
A Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data
2018
This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then a…
Temperate Fish Detection and Classification: a Deep Learning based Approach
2021
A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) …
Machine learning predictions of trophic status indicators and plankton dynamic in coastal lagoons
2018
Abstract Multivariate trophic indices provide an efficient way to assess and classify the eutrophication level and ecological status of a given water body, but their computation requires the availability of experimental information on many parameters, including biological data, that might not always be available. Here we show that machine learning techniques – once trained against a full data set – can be used to infer plankton biomass information from chemical and physical parameter only, so that trophic index can then be computed without using additional biological data. More specifically, we reconstruct plankton information from chemical and physical data, and this information together w…
Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea)
2021
This work presents a computational methodology able to automatically classify the echoes of two krill species recorded in the Ross sea employing scientific echo-sounder at three different frequencies (38, 120 and 200 kHz). The goal of classifying the gregarious species represents a time-consuming task and is accomplished by using differences and/or thresholds estimated on the energy features of the insonified targets. Conversely, our methodology takes into account energy, morphological and depth features of echo data, acquired at different frequencies. Internal validation indices of clustering were used to verify the ability of the clustering in recognizing the correct number of species. Th…
Gene flow relates to evolutionary divergence among populations at the range margin
2020
Background Morphological differentiation between populations resulting from local adaptations to environmental conditions is likely to be more pronounced in populations with increasing genetic isolation. In a previous study a positive clinal variation in body size was observed in isolated Roesel’s bush-cricket, Metrioptera roeselii, populations, but were absent from populations within a continuous distribution at the same latitudinal range. This observational study inferred that there was a phenotypic effect of gene flow on climate-induced selection in this species. Methods To disentangle genetic versus environmental drivers of population differences in morphology, we measured the size of …
Monitoring internet trade to inform species conservation actions
2017
Specimens, parts and products of threatened species are now commonly traded on the internet. This could threaten the survival of some wild populations if inadequately regulated. We outline two methods to monitor internet sales of threatened species in order to assess potential threats and inform conservation actions. Our first method combines systematic monitoring of online offers of plants for sale over the internet with consultation by experts experienced in identifying plants collected from the wild based on images of the specimens, species identity and details of the trade. Our second method utilises a computational model, trained using Bayesian techniques to records that have been clas…
The effect of maze complexity on maze-solving time in a desert ant
2019
One neglected aspect of research on foraging behavior is that of the effect of obstacles that increase habitat complexity on foraging efficiency. Here, we explored how long it takes individually foraging desert ant workers (Cataglyphis niger) to reach a food reward in a maze, and examined whether maze complexity affects maze-solving time (the time elapsed till the first worker reached the food reward). The test mazes differed in their complexity level, or the relative number of correct paths leading to the food reward, vs. wrong paths leading to dead-ends. Maze-solving time steeply increased with maze complexity, but was unaffected by colony size, despite the positive correlation between co…
Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks
2020
Abstract The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co‐acting…
Which traits allow weed species to persist in grass margin strips ?
2017
EASPEGESTADINRA; Sown-grass margin strips, historically established to limit pesticide drift and soil erosion, are now also promoted for enhancing floral diversity and associated ecosystem services. To better understand weed community assembly in grass margin strips, we performed floral surveys in 75 sown-grass margin strips in two regions in France and characterized each species using information from trait databases. We hypothesized that traits of dominant species would differ between newly sown-grass margin strips and older strips. Weed species were separated into functional groups based on their traits using multiple correspondence analysis and hierarchical ascendant classification. Fun…
Exploratory behaviour is not related to associative learning ability in the carabid beetle Nebria brevicollis.
2020
Abstract Recently, it has been hypothesised that as learning performance and animal personality vary along a common axis of fast and slow types, natural selection may act on both in parallel leading to a correlation between learning and personality traits. We examined the relationship between risk-taking, exploratory behaviour and associative learning ability in carabid beetle Nebria brevicollis females by quantifying the number of trials individuals required to reach criterion during an associative learning task (‘learning performance’). The associative learning task required the females to associate odour and direction with refugia from light and heat in a T-maze. Further, we assessed lea…