Seagrasses rank among the most productive yet highly threatened ecosystems on Earth. Loss of seagrass habitat because of anthropogenic disturbances and evidence of their limited resilience have provided the impetus for investigating and monitoring habitat restoration through transplantation programmes. Although Structure from Motion (SfM) photogrammetry is becoming a more and more relevant technique for mapping underwater environments, no standardised methods currently exist to provide 3-dimensional high spatial resolution and accuracy cartographic products for monitoring seagrass transplantation areas. By synthesizing various remote sensing applications, we provide an underwater SfM-based protocol for monitoring large seagrass restoration areas. The data obtained from consumer-grade red-green-blue (RGB) imagery allowed the fine characterization of the seabed by using 3D dense point clouds and raster layers, including orthophoto mosaics and Digital Surface Models (DSM). The integration of high spatial resolution underwater imagery with object-based image classification (OBIA) technique provided a new tool to count transplanted Posidonia oceanica fragments and estimate the bottom coverage expressed as a percentage of seabed covered by such fragments. Finally, the resulting digital maps were integrated into Geographic Information Systems (GIS) to run topographic change detection analysis and evaluate the mean height of transplanted fragments and detect fine-scale changes in seabed vector ruggedness measure (VRM). Our study provides a guide for creating large-scale, replicable and ready-to-use products for a broad range of applications aimed at standardizing monitoring protocols in future seagrass restoration actions.

Anthropogenic food patches in the marine environment, such as aquaculture farms and active trawlers, may impact on the behavior of marine mammals through modification of habitats, changes in predation pressure, or alterations in food distribution, availability, and predictability, affecting related social interactions and population demographics. This study examined patterns of association of a population of common bottlenose dolphins (Tursiops truncatus) around Lampedusa Island (Italy) during 1996–2006 and tested the hypothesis that the trawl fishery and the presence of an aquaculture farm could affect such patterns. Here, we used measures of association between pairs of individuals to assess this impact on social unit composition/cohesion and some analytical techniques to describe the structure of dolphin social networks and temporal stability of associations. Association information for 71 regularly sighted individuals was obtained from photo-identification surveys within groups observed or not at “feeding stations.” We found association patterns between dolphins were nonrandom. The Lampedusa population seems to be arranged into 6 clusters and organized in communities composed of animals that were either never seen in association with feeding stations (N individuals) or those that are (Y individuals), although mixed assemblages were also recorded. Both communities showed long-term preferred companions, with different degrees of social cohesion—as resulted by network measures and temporal analysis. Delineating community structure at Lampedusa Island has offered basic information for further investigations in the area, also providing novel evidences on how disparities in association patterns between bottlenose dolphin individuals may have resulted from a combination of ecological and anthropogenic factors.

Presence‐only data are typical occurrence information used in species distribution modelling. Data may be originated from different sources, and their integration is a challenging exercise in spatial ecology as detection biases are rarely fully considered. We propose a new protocol for presence‐only data fusion, where information sources include social media platforms, to investigate several possible solutions to reduce uncertainty in the modelling outputs. As a case study, we use spatial data on two dolphin species with different ecological characteristics and distribution, collected in central Tyrrhenian through traditional research campaigns and derived from a careful selection of social media images and videos. We built a spatial log‐Gaussian cox process that incorporates different detection functions and thinning for each data source. To finalize the model in a Bayesian framework, we specified priors for all model parameters. We used slightly informative priors to avoid identifiability issues when estimating both the animal intensity and the observation process. We compared different types of detection function and accessibility explanations. We showed how the detection function's variation affects ecological findings on two species representatives for different habitats and with different spatial distribution. Our findings allow for a sound understanding of the species distribution in the study area, confirming the proposed approach's appropriateness. Besides, the straightforward implementation in the R software, and the provision of examples' code with simulated data, consistently facilitate broader applicability of the method and allow for further validations. The proposed approach is widely functional and can be considered with different species and ecological contexts.