Reproduction, Fertility and Development is an international journal publishing original research , review and comment in the fields of reproduction and developmental biology in humans, domestic animals and wildlife

Biological control is an alternative strategy to control Cacopsylla bidens. The aim of this study was to untanglethe trophic network involving C. bidens as prey. Molecular techniques along with predator activity surveys were employed to evaluate predation on psylla. Feeding on C. bidens was detected for five predators: Chrysoperla externa, Chrysopidae sp., Hemerobiidae sp., Harmonia axyridis and Cycloneda sanguinea. All these predators begin to feed earlier in the season, apart from C. externawhich fedds from the third psylla generation. These results will allow the development of strategies to increase the presence of these predators in the orchards.

The ocean is a vast three-dimensional space that is poorly explored and understood, and harbors unobserved life and processes that are vital to ecosystem function. To fully interrogate the space, novel algorithms and robotic platforms are required to scale up observations. Locating animals of interest and extended visual observations in the water column are particularly challenging objectives. Towards that end, we present a novel Machine Learning-integrated Tracking (or ML-Tracking) algorithm for underwater vehicle control that builds on the class of algorithms known as tracking-by-detection. By coupling a multi-object detector (trained on in situ underwater image data), a 3D stereo tracker, and a supervisor module to oversee the mission, we show how ML-Tracking can create robust tracks needed for long duration observations, as well as enable fully automated acquisition of objects for targeted sampling. Using a remotely operated vehicle as a proxy for an autonomous underwater vehicle, we demonstrate continuous input from the ML-Tracking algorithm to the vehicle controller during a record, 5+ hr continuous observation of a midwater gelatinous animal known as a siphonophore. These efforts clearly demonstrate the potential that tracking-by-detection algorithms can have on exploration in unexplored environments and discovery of undiscovered life in our ocean.

This paper presents a comparison between some of the most used ranging localization methods based on the Received Signal Strength Indicator (RSSI) in low-power IEEE 802.15.4 wireless sensor networks. In particular, the Trilateration, the Min-Max and the Maximum-Likelihood algorithms have been compared using only a limited number of reference nodes. In order to perform an exhaustive comparison we carried out tests in an indoor environment: dozens of RSSI values for every estimation have been gathered and cleaned from outliers values. Our results show that it is possible to some extent to obtain positioning information from nodes equipped with IEEE 802.15.4 radio modules, given the position and the number of reference nodes.

There has been considerable discussion and planning in the oceanographic community toward the installation of long-term seafloor sites for scientific observation in the deep ocean. The Monterey Bay Aquarium Research Institute (MBARI) has designed a portable mooring system for deep ocean deployment that provides data and power connections to both seafloor and ocean surface instruments. The surface mooring collects solar and wind energy for powering instruments and transmits data to shore-side researchers using a satellite communications modem. A specialty anchor cable connects the surface mooring to a network of benthic instrumentation, providing the required data and power transfer. Design details and results of laboratory and field testing of the completed portions of the observatory system are described

MBARI's MOOS moored network is a state-of-the-art ocean observatory for interdisciplinary science, consisting of interconnected "host nodes" on the ocean surface, midwater, and seafloor. Each node can accommodate a wide variety of instruments. We describe some of the challenges posed by the system's functional requirements, and how those challenges are addressed by the design of the system's onboard hardware and software. Integration of diverse instruments and their protocols into the network poses major system integration and maintenance challenges, particularly in the areas of instrument installation and control, data retrieval, and data interpretation. All of these functions must be reliably performed in an often hostile environment on networked at-sea platforms that are generally power limited. We describe a distributed software architecture and implementation that addresses these challenges

The first MBARI shutter was developed to reduce the effect of biofouling (barnacles, etc.) on a variety of optical instruments in use on MBARI's oceanic moored platforms. While the first shutter has been quite effective, the second shutter is smaller, consumes less power, and is contained in a simple package, making it easier to integrate into other MBARI platforms.

Cabled observatories, such as MARS or the regional scale cabled observatory system planned for in the NSF Ocean Observatories Initiative (OOI), consist of many deployed instruments that communicate with human operators and shore-side data repositories. In addition, these deployed devices may actually communicate with one another, facilitating capabilities such as autonomous event response. These potentially complex interactions between multiple entities human and machine require that knowledge of the system configuration be available to participants. Users of instrument data require information metadata about the sensor that generated the data. Software that coordinates and controls instruments requires access to the software interfaces of those devices. "Manual configuration" has been used on small-scale systems, but in a network consisting of hundreds or thousands of instruments, the configuration challenge becomes critical. We propose to address the problem through automation of the configuration process, which will be achieved at several levels. Automated configuration will simplify the system operator's task of building and maintaining the observatory network. We describe a small, low-powered information storage device that we call a "instrument puck". When plugged into a suitable computer (lab workstation, deployed observing node), information can be written to or read from the puck. While an instrument is being prepared for initial integration into the observatory, a technician "loads" a puck with information necessary to configure the instrument within the observatory, and then physically attaches the puck to its instrument. Thereafter the attached puck always travels with its instrument, no matter where it is being installed in the observing network. The information loaded into the puck encompasses whatever is necessary to enable automatic configuration and system integration of the instrument when it is plugged into the observatory network, and any other information required by observatory policies. This information may include structured descriptions of the instrument's sensor and data characteristics (metadata). The information can also include actual software code that is retrieved from the puck and executed by an observatory node when the device is plugged in; this code could implement distributed instrument control and data retrieval interfaces, allowing network-wide access to the instrument functionality. We believe the puck concept to be a powerful one; a given instrument puck is configured just once, enabling automatic configuration of its instrument no matter where it is installed on the network thereafter. We also describe mechanisms by which an instrument and its puck can be "discovered" by the observatory network when the devices are plugged in. Several approaches are explored, with varying degrees of automation. We evaluate these approaches with special consideration to electrical and safety aspects of the undersea environment. Information and results from our prototyping efforts will also be presented.

Cabled observatories, such as MARS or the regional scale cabled observatory system planned for in the NSF Ocean Observatories Initiative (001), consist of many deployed instruments that communicate with human operators and shore-side data repositories. In addition, these deployed devices may actually communicate with one another, facilitating capabilities such as autonomous event response. These potentially complex interactions between multiple entities - human and machine - require that knowledge of the system configuration be available to participants. Users of instrument data require information - metadata - about the sensor that generated the data. Software that coordinates and controls instruments requires access to the software interfaces of those devices. "Manual configuration" has been used on small-scale systems, but in a network consisting of hundreds or thousands of instruments, the configuration challenge becomes critical. We propose to address the problem through automation of the configuration process, which will be achieved at several levels.Automated configuration will simplify the system operator's task of building and maintaining the observatory network. We describe a small, low-powered information storage device that we call a "instrument puck". When plugged into a suitable computer (lab workstation, deployed observing node), information can be written to or read from the puck. While an instrument is being prepared for initial integration into the observatory, a technician "loads" a puck with information necessary to configure the instrument within the observatory, and then physically attaches the puck to its instrument. Thereafter the attached puck always travels with its instrument, no matter where it is being installed in the observing network.The information loaded into the puck encompasses whatever is necessary to enable automatic configuration and system integration of the instrument when it is plugged into the observatory network, and any other information required by observatory policies. This information may include structured descriptions of the instrument's sensor and data characteristics (metadata). The information can also include actual software code that is retrieved from the puck and executed by an observatory node when the device is plugged in; this code could implement distributed instrument control and data retrieval interfaces, allowing network-wide access to the instrument functionality. We believe the puck concept to be a powerful one; a given instrument puck is configured just once, enabling automatic configuration of its instrument no matter where it is installed on the network thereafter.We also describe mechanisms by which an instrument and its puck can be "discovered" by the observatory network when the devices are plugged in. Several approaches are explored, with varying degrees of automation. We evaluate these approaches with special consideration to electrical and safety aspects of the undersea environment. Information and results from our prototyping efforts will also be presented.

We present an attentional selection system for processing video streams from remotely operated underwater vehicles (ROVs). The system identifies potentially interesting visual events spanning multiple frames based on low-level spatial properties of salient tokens, which are associated with those events and tracked over time. If video frames contain interesting events, they are labeled "interesting", otherwise they are labeled "boring". By marking the interesting events and omitting "boring" frames in the output stream, we augment the productivity of human video annotators, or, alternatively, provide input for a subsequent object classification algorithm.

The MBARI Ocean Observing System (MOOS) will consist of networked observation platforms and sensors deployed over a wide geographic area, distributed throughout the oceanic water column. The network will utilize a variety of communication links, including optical fiber, microwave, packet radio, satellite, and acoustic, resulting in diversity of throughput, latency, and intermittence throughout the network. The network membership will be highly dynamic and unpredictable, as links go "up" and "down", and devices are added to and removed from the network. The sensors themselves will include a wide range of off-the-shelf instruments as will as novel devices developed at MBARI and elsewhere; sensor interface protocols will thus be very diverse, as there are currently no widely recognized standards. These aspects of the ocean observing system network present challenging software engineering problems. The authors review available "smart network" software technologies that address these problems, and evaluate their feasibility for their system. Addressing the diversity of sensors and protocols, they describe a device called a sensor puck, that could provide a universal interface between any sensor and the network, and that enables spontaneous configuration and operation when the sensor is plugged into the network.