Why do dolphins form mixed-species associations in the Azores ?

Mixed-species associations are temporary associations between individuals of different species that are often observed in birds, primates and cetaceans. They have been interpreted as a strategy to reduce predation risk, enhance foraging success and/or provide a social advantage. In the archipelago of the Azores, four species of dolphins are commonly involved in mixed-species associations: the common dolphin, Delphinus delphis, the bottlenose dolphin, Tursiops truncatus, the striped dolphin, Stenella coeruleoalba, and the spotted dolphin, Stenella frontalis. In order to understand the reasons why dolphins associate, we analysed field data collected since 1999 by research scientists and trained observers placed onboard fishing vessels. In total, 113 mixed-species groups were observed out of 5720 sightings. The temporal distribution, habitat (water depth, distance to the coast), behaviour (i.e. feeding, travelling, socializing), size and composition of mixed-species groups were compared with those of single-species groups. Results did not support the predation avoidance hypothesis and gave little support to the social advantage hypothesis. The foraging advantage hypothesis was the most convincing. However, the benefits of mixed-species associations appeared to depend on the species. Associations were likely to be opportunistic in the larger bottlenose dolphin, while there seemed to be some evolutionary constraints favouring associations in the rarer striped dolphin. Comparison with previous studies suggests that the formation of mixed-species groups depends on several environmental factors, and therefore may constitute an adaptive response

Environmental representativity in marine protected area networks over large and partly unexplored seascapes

Converting assemblages of marine protected areas (MPAs) into functional MPA networks requires political will, multidisciplinary information, coordinated action and time. We developed a new framework to assist planning environmental representativity in a network across the marine space of Portugal, responding to a political commitment to protect 14% of its area by 2020. An aggregate conservation value was estimated for each of the 27 habitats identified, from intertidal waters to the deep sea. This value was based on expert-judgment scoring for environmental properties and features relevant for conservation, chosen to reflect the strategic objectives of the network, thus providing an objective link between conservation commitments and habitat representativity in space. Additionally, habitats' vulnerability to existing anthropogenic pressures and sensitivity to climate change were also scored. The area coverage of each habitat in Portugal and within existing MPAs (regionally and nationally) was assigned to a scale of five orders of magnitude (from 10%) to assess rarity and existing representation. Aggregate conservation value per habitat was negatively correlated with area coverage, positively correlated with vulnerability and was not correlated with sensitivity. The proposed framework offers a multi-dimensional support tool for MPA network development, in particular regarding the prioritization of new habitats to protect, when the goal is to achieve specific targets while ensuring representativity across large areas and complex habitat mosaics. It requires less information and computation effort in comparison to more quantitative approaches, while still providing an objective instrument to scrutinize progress on the implementation of politically set conservation targets.Agência financiadora Número do subsídio Oceanic Observatory of Madeira M1420-01-0145-FEDER-000001-OOM national funds through FCT UID/BIA/00329/2013 UID/Multi/04326/2013 Fundacao para a Ciencia e a Tecnologia SFRH/BPD/95334/2013 CESAM - FCT/MEC through national funds UID/AMB/50017 - POCI-01-0145-FEDER-007638 FEDER FCT SFRH/BPD/94320/2013 MARE - UID/MAR/04292/2019 EU through the Cohesion Fund POSEUR-03-2215-FC-000046 POSEUR-03-2215-FC-000047 FCT national funds ECO/28687/2017info:eu-repo/semantics/publishedVersio

Advancing multi-vehicle deployments in oceanographic field experiments

Our research concerns the coordination and control of robotic vehicles for upper water-column oceanographic observations. In such an environment, operating multiple vehicles to observe dynamic oceanographic phenomena, such as ocean processes and marine life, from fronts to cetaceans, has required that we design, implement and operate software, methods and processes which can support opportunistic needs in real-world settings with substantial constraints. In this work, an approach for coordinated measurements using such platforms, which relate directly to task outcomes, is presented. We show the use and operational value of a new Artificial Intelligence based mixed-initiative system for handling multiple platforms along with the networked infrastructure support needed to conduct such operations in the open sea. We articulate the need and use of a range of middleware architectures, critical for such deployments and ground this in the context of a field experiment in open waters of the mid-Atlantic in the summer of 2015.Advancing multi-vehicle deployments in oceanographic field experimentsacceptedVersio

NEOTROPICAL XENARTHRANS: a data set of occurrence of xenarthran species in the Neotropics

Xenarthrans – anteaters, sloths, and armadillos – have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with 24 domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, ten anteaters, and six sloths. Our dataset includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data-paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the south of the USA, Mexico, and Caribbean countries at the northern portion of the Neotropics, to its austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n=5,941), and Cyclopes sp. has the fewest (n=240). The armadillo species with the most data is Dasypus novemcinctus (n=11,588), and the least recorded for Calyptophractus retusus (n=33). With regards to sloth species, Bradypus variegatus has the most records (n=962), and Bradypus pygmaeus has the fewest (n=12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other datasets of Neotropical Series which will become available very soon (i.e. Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans dataset

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio