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Aquatic ecologists face challenges in identifying the general rules of the functioning of ecosystems. A common framework, including freshwater, marine, benthic, and pelagic ecologists, is needed to bridge communication gaps and foster knowledge sharing. This framework should transcend local specificities and taxonomy in order to provide a common ground and shareable tools to address common scientific challenges. Here, we advocate the use of functional trait‐based approaches (FTBAs) for aquatic ecologists and propose concrete paths to go forward. Firstly, we propose to unify existing definitions in FTBAs to adopt a common language. Secondly, we list the numerous databases referencing functional traits for aquatic organisms. Thirdly, we present a synthesis on traditional as well as recent promising methods for the study of aquatic functional traits, including imaging and genomics. Finally, we conclude with a highlight on scientific challenges and promising venues for which FTBAs should foster opportunities for future research. By offering practical tools, our framework provides a clear path forward to the adoption of trait‐based approaches in aquatic ecology.

We present what we believe to be a new algorithm, FRactal Iterative Method (FRiM), aiming at the reconstruction of the optical wavefront from measurements provided by a wavefront sensor. As our application is adaptive optics on extremely large telescopes, our algorithm was designed with speed and best quality in mind. The latter is achieved thanks to a regularization that enforces prior statistics. To solve the regularized problem, we use the conjugate gradient method, which takes advantage of the sparsity of the wavefront sensor model matrix and avoids the storage and inversion of a huge matrix. The prior covariance matrix is, however, non-sparse, and we derive a fractal approximation to the Karhunen–Loève basis thanks to which the regularization by Kolmogorov statistics can be computed in O(N) operations, with N being the number of phase samples to estimate. Finally, we propose an effective preconditioning that also scales as O(N) and yields the solution in five to ten conjugate gradient iterations for any N. The resulting algorithm is therefore O(N). As an example, for a 128×128 Shack–Hartmann wavefront sensor, the FRiM appears to be more than 100 times faster than the classical vector-matrix multiplication method.

In ground-based astronomy, the inverse problem of phase retrieval from speckle images is a means to calibrate static aberrations for correction by active optics. It can also be used to sense turbulent wavefronts. However, the number of local minima drastically increases with the turbulence strength, mainly because of phase wrapping ambiguities. Multifocal phase diversity has been considered to overcome some ambiguities of the phase retrieval problem. We propose an effective algorithm for phase retrieval from a single focused image. Our algorithm makes use of a global optimization strategy and an automatically tuned smoothness prior to overcome local minima and phase degeneracies. We push the limit of D/r_0=4 achieved by Irwan and Lane [J. Opt. Soc. Am. A.15, 2302 (1998)] up to D/r_0=11, which is a major improvement owing to the drastic increase in the problem complexity. We estimate the performances of our approach from consistent simulations for different turbulence strengths and noise levels (down to 1500 photons per image). We also investigate the benefit of temporal correlation.

We report the photometric observation of a polychromatic laser guide star (PLGS) using the AVLIS laser at the Lawrence Livermore National Laboratory (LLNL). The process aims at providing a measurement of the tilt of the incoming wave front at a telescope induced by atmospheric turbulence. It relies on the two-photon coherent excitation of the 4D_5/2 energy level of sodium atoms in the mesosphere. We used two laser beams at 589 and 569 nm, with a maximum total average output power of ≈350 W. For the purpose of photometric calibration, a natural star was observed simultaneously through the same instrument as the PLGS at the focus of the LLNL 50-cm telescope. Photometric measurements of the 330-nm return flux confirm our previous theoretical studies that the PLGS process should allow us at a later stage to correct for the tilt at wavelengths as short as ≈1 µm at good astronomical sites. They show also that, at saturation of two-photon coherent absorption in the mesosphere, the backscattered flux increases by a factor of ≈2 when the pulse repetition rate decreases by a factor of 3 at constant average power. This unexpected behavior is briefly discussed.

Real photon-counting detectors are unable to distinguish overlapping photon events. This defect limits considerably the performance of speckle-imaging methods. In particular, this defect results in the so-called photon-counting hole in the center of the average image autocorrelation. Cross-correlation techniques are investigated, and it is demonstrated that they avoid the photon-counting defect for the purpose of speckle imaging with the bispectrum and the Knox–Thompson methods. Furthermore, a method that requires the recording of only two sets of simultaneous images is proposed for obtaining a quasi-unbiased estimator of the bispectrum. The validity of these methods is checked against simulated data and under various flux levels. Signal-to-noise-ratio considerations are addressed.

Inline digital holograms are classically reconstructed using linear operators to model diffraction. It has long been recognized that such reconstruction operators do not invert the hologram formation operator. Classical linear reconstructions yield images with artifacts such as distortions near the field-of-view boundaries or twin images. When objects located at different depths are reconstructed from a hologram, in-focus and out-of-focus images of all objects superimpose upon each other. Additional processing, such as maximum-of-focus detection, is thus unavoidable for any successful use of the reconstructed volume. In this Letter, we consider inverting the hologram formation model in a Bayesian framework. We suggest the use of a sparsity-promoting prior, verified in many inline holography applications, and present a simple iterative algorithm for 3D object reconstruction under sparsity and positivity constraints. Preliminary results with both simulated and experimental holograms are highly promising.