Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis

AbstractLameness causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system (AMS) to model on-farm gait scoring from a commercial farm. A total of 88 cows were gait scored once per week, for 2 5-wk periods. Eighty variables retrieved from AMS were summarized week-wise and used to predict 2 defined classes: nonlame and clinically lame cows. Variables were represented with 2 transformations of the week summarized variables, using 2-wk data blocks before gait scoring, totaling 320 variables (2×2×80). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3 or 4/4) or not lame (score 1/4). Both models achieved sensitivity and specificity values around 80%, both in calibration and cross-validation. At the optimum values in the receiver operating characteristic curve, the false-positive rate was 28% in the parity 1 model, whereas in the parity 2 model it was about half (16%), which makes it more suitable for practical application; the model error rates were, 23 and 19%, respectively. Based on data registered automatically from one AMS farm, we were able to discriminate nonlame and lame cows, where partial least squares discriminant analysis achieved similar performance to the reference method

Methods for the Study of Marine Biodiversity

Recognition of the threats to biodiversity and its importance to society has led to calls for globally coordinated sampling of trends in marine ecosystems. As a step to defining such efforts, we review current methods of collecting and managing marine biodiversity data. A fundamental component of marine biodiversity is knowing what, where, and when species are present. However, monitoring methods are invariably biased in what taxa, ecological guilds, and body sizes they collect. In addition, the data need to be placed, and/or mapped, into an environmental context. Thus a suite of methods will be needed to encompass representative components of biodiversity in an ecosystem. Some sampling methods can damage habitat and kill species, including unnecessary bycatch. Less destructive alternatives are preferable, especially in conservation areas, such as photography, hydrophones, tagging, acoustics, artificial substrata, light-traps, hook and line, and live-traps. Here we highlight examples of operational international sampling programmes and data management infrastructures, notably the Continuous Plankton Recorder, Reef Life Survey, and detection of Harmful Algal Blooms and MarineGEO. Data management infrastructures include the World Register of Marine Species for species nomenclature and attributes, the Ocean Biogeographic Information System for distribution data, Marine Regions for maps, and Global Marine Environmental Datasets for global environmental data. Existing national sampling programmes, such as fishery trawl surveys and intertidal surveys, may provide a global perspective if their data can be integrated to provide useful information. Less utilised and emerging sampling methods, such as artificial substrata, light-traps, microfossils and eDNA also hold promise for sampling the less studied components of biodiversity. All of these initiatives need to develop international standards and protocols, and long-term plans for their governance and support.published_or_final_versio

GlobalHAB (IOC-UNESCO and SCOR): Latinamerica contribution to the international coordination for sound knowledge of HABs to manage their impacts

12th International Phycological CongressThe Global Harmful Algal Blooms (GlobalHAB, www.global hab.info) Program is aimed at fostering international cooperative research directed toward improving the prediction of harmful algal bloom (HAB) events in aquatic ecosystems, and providing sound knowledge for policy- and decision-making to manage and mitigate HAB impacts in a changing planet. GlobalHAB is sponsored by the Intergovernmental Oceanographic Commission (IOC) of UNESCO and the Scientific Committee on Oceanic Research (SCOR). GlobalHAB was launched in 2016 and will last for 10 years. The GlobalHAB scientific objectives are focused on the research of taxonomic, ecological and toxicology knowledge gaps, on the effects of climate change on HABs and their biogeographic distribution, the implementation of HABs observing systems, and overall, to promote aquatic food and water safety and security. The GlobalHAB program has an international nature, and collaborates with international entities and programs that share objectives on HABs research, management and mitigation, as was already done by the former program GEOHAB. In particular, scientists from Latin America were active participants in GEOHAB and today Latin America is key in the implementation of GlobalHAB. Extreme HAB events affecting aquaculture sites and natural environments, Sargassum beachings, HABs monitoring programs, ciguatera fish poisoning, toxin transfer through the food webs, are examples of topics where scientists in Latin America are very active and thus, contribute to the implementation of GlobalHAB. Scientists are invited to participate in GlobalHAB by designing and endorsing scientific activities linked to the goals of GlobalHAB, and by participating in other international activitiesThe GlobalHAB (www.globalhab.info) international program is funded by IOC UNESCO and SCORPeer reviewe