As animals vocalize, their vocal organ transforms motor commands into vocalizations for social communication. In birds, the physical mechanisms by which vocalizations are produced and controlled remain unresolved because of the extreme difficulty in obtaining in vivo measurements. Here, we introduce an ex vivo preparation of the avian vocal organ that allows simultaneous high-speed imaging, muscle stimulation and kinematic and acoustic analyses to reveal the mechanisms of vocal production in birds across a wide range of taxa. Remarkably, we show that all species tested employ the myoelastic-aerodynamic (MEAD) mechanism, the same mechanism used to produce human speech. Furthermore, we show substantial redundancy in the control of key vocal parameters ex vivo, suggesting that in vivo vocalizations may also not be specified by unique motor commands. We propose that such motor redundancy can aid vocal learning and is common to MEAD sound production across birds and mammals, including humans.

The accelerated global losses of seagrass meadows makes restoration increasingly important. This restoration study was conducted in a shallow Danish estuary and describes one of the rare examples of successful large-scale eelgrass Zostera marina restoration outside North America. A simplified 3-step site selection approach was successfully applied to locate an optimal site for large-scale transplantation. It consisted of (1) qualitative assessments of vegetation using aerial photos, (2) inspection of potential sites with assessments of stressor presence and potential growth conditions and (3) transplantation tests for a final assessment of site suitability and methodology. The large-scale transplantation was initiated at the test site with the highest shoot production. After transplantation, shoot densities developed rapidly, achieving a 70-fold increase in density after about 2 yr. A rapid edge expansion (0.32 m yr-1) of the transplanted area was detected using drone-based monitoring. Both the final shoot density and edge expansion were comparable to those of natural eelgrass patches in the estuary. Eelgrass-transplanted areas accumulated more fine sediment particles and organic C, N and P than adjacent unvegetated sediment. Burial of organic C, N and P in eelgrass-transplanted sediments was 33 ± 7.5, 6.6 ± 0.9 and 3.0 ± 0.5 g m-2 yr-1, respectively (mean ± SE). In addition, inorganic C and N were assimilated by eelgrass transplants at rates of 290 ± 22 and 12 ± 1.0 g m-2 yr-1, respectively. The results highlight that important ecosystem services are already restored 2 yr after successful eelgrass restoration.

Traditional monitoring (e.g., in-water based surveys) of eelgrass meadows and perennial macroalgae in coastal areas is time and labor intensive, requires extensive equipment, and the collected data has a low temporal resolution. Further, divers and Remotely Operated Vehicles (ROVs) have a low spatial extent that cover small fractions of full systems. The inherent heterogeneity of eelgrass meadows and macroalgae assemblages in these coastal systems makes interpolation and extrapolation of observations complicated and, as such, methods to collect data on larger spatial scales whilst retaining high spatial resolution is required to guide management. Recently, the utilization of Unoccupied Aerial Vehicles (UAVs) has gained popularity in ecological sciences due to their ability to rapidly collect large amounts of area-based and georeferenced data, making it possible to monitor the spatial extent and status of SAV communities with limited equipment requirements compared to ROVs or diver surveys. This paper is focused on the increased value provided by UAV-based, data collection (visual/Red Green Blue imagery) and Object Based Image Analysis for gaining an improved understanding of eelgrass recovery. It is demonstrated that delineation and classification of two species of SAV ( Fucus vesiculosus and Zostera marina) is possible; with an error matrix indicating 86–92% accuracy. Classified maps also highlighted the increasing biomass and areal coverage of F. vesiculosus as a potential stressor to eelgrass meadows. Further, authors derive a statistically significant conversion of percentage cover to biomass ( R2 = 0.96 for Fucus vesiculosus, R2 = 0.89 for Zostera marina total biomass, and R2 = 0.94 for AGB alone, p < 0.001). Results here provide an example of mapping cover and biomass of SAV and provide a tool to undertake spatio-temporal analyses to enhance the understanding of eelgrass ecosystem dynamics.

Information of the physical and ecological state of streams along with an overview of the need for maintenance is traditionally a time-consuming manual field task with subsequent limitations in area coverage. Here we propose a novel approach to stream monitoring and management using a low-cost Unmanned Aerial Vehicle (UAV) platform to collect data comparable to that from traditional monitoring schemes. This technology provides high resolution imagery while being easy to implement at a low cost along with providing data that represent the stream in both fine scale and at landscape scale. The results show a significant correlation between results obtained by the two methods, with the largest difference in DFI-values being ten, but in many cases being < five. The UAV-method is especially strong in supporting geographical measurements of stream width and course along with certain stream parameters such as physical variation, water flow and gravel coverage. The results indicate that UAV mapping of streams is a feasible alternative or support to the traditional mapping of certain open stream types with the possibility of covering more area with the same time-use.

Knowledge about the spatial distribution of seagrasses is essential for coastal conservation efforts. Imagery obtained from unoccupied aerial systems (UAS) has the potential to provide such knowledge. Classifier choice and hyperparameter settings are, however, often based on time-consuming trial-and-error procedures. The presented study has therefore investigated the performance of five machine learning algorithms, i.e., Bayes, Decision Trees (DT), Random Trees (RT), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) when used for the object-based classification of submerged seagrasses from UAS-derived imagery. The influence of hyperparameter tuning and training sample size on the classification accuracy was tested on images obtained from different altitudes during different environmental conditions. The Bayes classifier performed well (94% OA) on images obtained during favorable environmental conditions. The DT and RT classifier performed better on low-altitude images (93% and 94% OA, respectively). The kNN classifier was outperformed on all occasions, while still producing OA between 89% and 95% in five out of eight scenarios. The SVM classifier was most sensitive to hyperparameter tuning with OAs ranging between 18% and 97%; however, it achieved the highest OAs most often. The findings of this study will help to choose the appropriate classifier and optimize related hyperparameter settings.