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Benthic components of tropical mesophotic coral ecosystems (MCEs) are home to diverse fish assemblages, but the effect of multiscale spatial benthic characteristics on MCE fish is not well understood. To investigate the influence of fine-scale benthic seascape structure and broad-scale environmental characteristics on MCE fish, we surveyed fish assemblages in Seychelles at 30, 60 and 120 m depth using submersible video transects. Spatial pattern metrics from seascape ecology were applied to quantify fine-scale benthic seascape composition, configuration and terrain morphology from structure-from-motion photogrammetry and multibeam echosounder bathymetry and to explore seascape–fish associations. Hierarchical clustering using fish abundance and biomass data identified four distinct assemblages separated by the depth and geographic location, but also significantly influenced by variations in fine-scale seascape structure. Results further revealed variable responses of assemblage characteristics (fish biomass, abundance, trophic group richness, Shannon diversity) to seascape heterogeneity at different depths. Sites with steep slopes and high terrain complexity hosted higher fish abundance and biomass, with shallower fish assemblages (30–60 m) positively associated with aggregated patch mixtures of coral, rubble, sediment and macroalgae with variable patch shapes. Deeper fish assemblages (120 m) were positively associated with relief and structural complexity and local variability in the substratum and benthic cover. Our study demonstrates the potential of spatial pattern metrics quantifying benthic composition, configuration and terrain structure to delineate mesophotic fish–habitat associations. Furthermore, incorporating a finer-scale perspective proved valuable to explain the compositional patterns of MCE fish assemblages. As developments in marine surveying and monitoring of MCEs continue, we suggest that future studies incorporating spatial pattern metrics with multiscale remotely sensed data can provide insights will that are both ecologically meaningful to fish and operationally relevant to conservation strategies.

The so-called block-term decomposition (BTD) tensor model, especially in its rank-$(L_r,L_r,1)$ version, has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are composed of \emph{blocks} of rank higher than one, a scenario encountered in numerous and diverse applications. Uniqueness conditions and fitting methods have thus been thoroughly studied. Nevertheless, the challenging problem of estimating the BTD model structure, namely the number of block terms, $R$, and their individual ranks, $L_r$, has only recently started to attract significant attention, mainly through regularization-based approaches which entail the need to tune the regularization parameter(s). In this work, we build on ideas of sparse Bayesian learning (SBL) and put forward a fully automated Bayesian approach. Through a suitably crafted multi-level \emph{hierarchical} probabilistic model, which gives rise to heavy-tailed prior distributions for the BTD factors, structured sparsity is \emph{jointly} imposed. Ranks are then estimated from the numbers of blocks ($R$) and columns ($L_r$) of non-negligible energy. Approximate posterior inference is implemented, within the variational inference framework. The resulting iterative algorithm completely avoids hyperparameter tuning, which is a significant defect of regularization-based methods. Alternative probabilistic models are also explored and the connections with their regularization-based counterparts are brought to light with the aid of the associated maximum a-posteriori (MAP) estimators. We report simulation results with both synthetic and real-word data, which demonstrate the merits of the proposed method in terms of both rank estimation and model fitting as compared to state-of-the-art relevant methods.

We consider the opportunities and challenges associated with organizing a conference online, using a case study of a medium-sized (approx. 400 participants) international conference held virtually in August 2020. In addition, we present quantifiable evidence of the participants' experience using the results from an online post-conference questionnaire. Although the virtual meeting was not able to replicate the in-person experience in some aspects (e.g. less engagement between participants) the overwhelming majority of respondents found the meeting an enjoyable experience and would join similar events again. Notably, there was a strong desire for future in-person meetings to have at least some online component. Online attendance by lower-income researchers was higher compared with a past, similar-themed in-person meeting held in a high-income nation, but comparable to one held in an upper-middle-income nation. This indicates that online conferences are not a panacea for diversity and inclusivity, and that holding in-person meetings in developing economies can be at least as effective. Given that it is now relatively easy to stream contents of meetings online using low-cost methods, there are clear benefits in making all presented content accessible online, as well as organizing online networking events for those unable to attend in person.

Background During the 2019 First Descent: Seychelles Expedition, shallow and deep reef ecosystems of the Seychelles Outer Islands were studied by deploying a variety of underwater technologies to survey their benthic flora and fauna. Submersibles, remotely operated vehicles (ROVs) and SCUBA diving teams used stereo-video camera systems to record benthic communities during transect surveys conducted at 10 m, 30 m, 60 m, 120 m, 250 m and 350 m depths. In total, ~ 45 h of video footage was collected during benthic transect surveys, which was subsequently processed using annotation software in order to assess reef biodiversity and community composition. Here, we present a photographic guide for the visual identification of the marine macrophytes, corals, sponges and other common invertebrates that inhabit Seychelles’ reefs. It is hoped that the resulting guide will aid marine biologists, conservationists, managers, divers and naturalists with the coarse identification of organisms as seen in underwater footage or live in the field. New information A total of 184 morphotypes (= morphologically similar individuals) were identified belonging to Octocorallia (47), Porifera (35), Scleractinia (32), Asteroidea (19), Echinoidea (10), Actiniaria (9), Chlorophyta (8), Antipatharia (6), Hydrozoa (6), Holothuroidea (5), Mollusca (2), Rhodophyta (2), Tracheophyta (2), Annelida (1), Crinoidea (1), Ctenophora (1), Ochrophyta (1) and Zoantharia (1). Out of these, we identified one to phylum level, eight to class, 14 to order, 27 to family, 110 to genus and 24 to species. This represents the first attempt to catalogue the benthic diversity from shallow reefs and up to 350 m depth in Seychelles.

The so-called block-term decomposition (BTD) tensor model has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are composed of blocks of rank higher than one, a scenario encountered in numerous and diverse applications. In this paper, BTD is employed for the completion of a tensor from its partially observed entries. A novel method is proposed, which is based on the idea of imposing column sparsity jointly on the BTD factors and in a hierarchical manner. This way the number of block terms and their ranks can also be estimated, as the numbers of factor columns of non-negligible magnitude. Following a block successive upper bound minimization (BSUM) approach with appropriate choice of the surrogate majorizing functions is shown to result in an alternating hierarchical iteratively reweighted least squares (HIRLS) algorithm, which is fast converging and enjoys high computational efficiency, as it relies in its iterations on small-sized sub-problems with closed-form solutions. Simulation results with both synthetic and real data are reported, which demonstrate the effectiveness of the proposed scheme.

Nowadays, the availability of large-scale data in disparate application domains urges the deployment of sophisticated tools for extracting valuable knowledge out of this huge bulk of information. In that vein, low-rank representations (LRRs), which seek low-dimensional embeddings of data have naturally appeared. In an effort to reduce computational complexity and improve estimation performance, LRR has been viewed via a matrix factorization (MF) perspective. Recently, low-rank MF (LRMF) approaches have been proposed for tackling the inherent weakness of MF, i.e., the unawareness of the dimension of the low-dimensional space where data reside. Herein, inspired by the merits of iterative reweighted schemes for sparse recovery and rank minimization, we come up with a generic low-rank promoting regularization function. Then, focusing on a specific instance of it, we propose a regularizer that imposes column-sparsity jointly on the two matrix factors that result from MF, thus promoting low-rankness on the optimization problem. The low-rank promoting properties of the resulting regularization term are brought to light by mathematically showing that it is actually a tight upper bound of a specific version of the weighted nuclear norm. The problems of denoising and matrix completion are redefined according to the new LRMF formulation and solved via efficient alternating iteratively reweighted least squares type algorithms. Theoretical analysis of the algorithms regarding the convergence and the rates of convergence to stationary points is provided. The effectiveness of the proposed algorithms is verified in diverse simulated and real data experiments.

Inadequate and inequitable distribution of research capacity and resources limits both the opportunity for leadership and participation in science. It also results in biases of effort, poor and misinterpretation of global patterns and the availability of limited usable knowledge for current challenges. Increased participation in ocean research and decision-making is needed to account for many stressors and challenges. The current intergovernmental attention on the ocean (e.g. UN Decade of Ocean Science for Sustainable Development) and the development of technologies that permit exploration and accelerate exploitation suggest that it is timely to focus on the ocean and its stewardship. Employing the principles of co-development, co-production and co-dissemination, this paper uses a case study of a deep reef project in Seychelles to illustrate some activities that can be employed to magnify research outcomes and legacy. We provide examples that range from ministerial briefings and planning meetings to joint fieldwork, grant allocation and co-authoring outputs. These activities helped us to align priorities, promote authentic interactions and focus on equitable science. Finally, reflecting on our experiences, we acknowledge the benefits brought by respectful and long-term partnerships, the variety of activities needed to develop these and challenges of maintaining them. In the future, we also want to include more opportunities for regional peer-to-peer learning and technology transfer.

Parachute science is the practice whereby international scientists, typically from higher-income countries, conduct field studies in another country, typically of lower income, and then complete the research in their home country without any further effective communication and engagement with others from that nation. It creates dependency on external expertise, does not address local research needs, and hinders local research efforts. As global hotspots of marine biodiversity, lower-income nations in the tropics have for too long been the subject of inequitable and unfair research practices1. However, to date there has been little quantifiable evidence of this phenomenon in marine science. Here, we provide evidence through systematic literature searches and queries that parachute science practices are still widespread in marine research and make some recommendations to help change the current status quo. VIDEO ABSTRACT.

The so-called block-term decomposition (BTD) tensor model has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are composed of blocks of rank higher than one, a scenario encountered in numerous and diverse applications. Its uniqueness and approximation have thus been thoroughly studied. Nevertheless, the challenging problem of estimating the BTD model structure, namely the number of block terms and their individual ranks, has only recently started to attract significant attention. In this paper, a novel method of BTD model selection and computation is proposed, based on the idea of imposing column sparsity jointly on the factors and in a hierarchical manner and estimating the ranks as the numbers of factor columns of non-negligible magnitude. Following a block successive upper bound minimization (BSUM) approach for the proposed optimization problem is shown to result in an alternating hierarchical iteratively reweighted least squares (HIRLS) algorithm, which is fast converging and enjoys high computational efficiency, as it relies in its iterations on small-sized sub-problems with closed-form solutions. Simulation results for both synthetic examples and a hyper-spectral image denoising application are reported, which demonstrate the superiority of the proposed scheme over the state-of-the-art in terms of success rate in rank estimation as well as computation time and rate of convergence while attaining a comparable tensor approximation performance.

18 pages, 5 figures, supplementary data https://doi.org/10.1016/j.biocon.2021.109175.-- The data supporting the findings of this study are available in the Supplementary Materials (Appendix 3–5, Table A3-A5). Raw datasets (where available) and results summary tables for each analysis of human mobility and empirical datasets are deposited in a github repository: https://github.com/rjcommand/PAN-Environment, The global lockdown to mitigate COVID-19 pandemic health risks has altered human interactions with nature. Here, we report immediate impacts of changes in human activities on wildlife and environmental threats during the early lockdown months of 2020, based on 877 qualitative reports and 332 quantitative assessments from 89 different studies. Hundreds of reports of unusual species observations from around the world suggest that animals quickly responded to the reductions in human presence. However, negative effects of lockdown on conservation also emerged, as confinement resulted in some park officials being unable to perform conservation, restoration and enforcement tasks, resulting in local increases in illegal activities such as hunting. Overall, there is a complex mixture of positive and negative effects of the pandemic lockdown on nature, all of which have the potential to lead to cascading responses which in turn impact wildlife and nature conservation. While the net effect of the lockdown will need to be assessed over years as data becomes available and persistent effects emerge, immediate responses were detected across the world. Thus initial qualitative and quantitative data arising from this serendipitous global quasi-experimental perturbation highlights the dual role that humans play in threatening and protecting species and ecosystems. Pathways to favorably tilt this delicate balance include reducing impacts and increasing conservation effectiveness, The Canada Research Chairs program provided funding for the core writing team. Field research funding was provided by A.G. Leventis Foundation; Agence Nationale de la Recherche, [grant number ANR-18-32–0010CE-01 (JCJC PEPPER)]; Agencia Estatal de Investigaci; Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação (ARDITI), [grant number M1420-09-5369-FSE-000002]; Alan Peterson; ArcticNet; Arkadaşlar; Army Corp of Engineers; Artificial Reef Program; Australia's Integrated Marine Observing System (IMOS), National Collaborative; Research Infrastructure Strategy (NCRIS), University of Tasmania; Australian Institute of Marine Science; Australian Research Council, [grant number LP140100222]; Bai Xian Asia Institute; Batubay Özkan; BC Hydro Fish and Wildlife Compensation Program; Ben-Gurion University of the Negev; Bertarelli Foundation; Bertarelli Programme in Marine Science; Bilge Bahar; Bill and Melinda Gates Foundation; Biology Society of South Australia; Boston University; Burak Över; California State Assembly member Patrick O'Donnell; California State University Council on Ocean Affairs, Science & Technology; California State University Long Beach; Canada Foundation for Innovation (Major Science Initiative Fund and funding to Oceans Network Canada), [grant number MSI 30199 for ONC]; Cape Eleuthera Foundation; Centre National d'Etudes Spatiales; Centre National de la Recherche Scientifique; Charles Darwin Foundation, [grant number 2398]; Colombian Institute for the Development of Science and Technology (COLCIENCIAS), [grant number 811–2018]; Colombian Ministry of Environment and Sustainable Development, [grant number 0041–2020]; Columbia Basin Trust; Commission for Environmental Cooperation; Cornell Lab of Ornithology; Cultural practices and environmental certification of beaches, Universidad de la Costa, Colombia, [grant number INV.1106–01–002-15, 2020–21]; Department of Conservation New Zealand; Direction de l'Environnement de Polynésie Française; Disney Conservation Fund; DSI-NRF Centre of; Excellence at the FitzPatrick Institute of African Ornithology; Ecology Project International; Emin Özgür; Environment and Climate Change Canada; European Community: RTD programme - Species Support to Policies; European Community's Seventh Framework Programme; European Union; European Union's Horizon 2020 research and innovation programme, Marie Skłodowska-Curie, [grant number 798091, 794938]; Faruk Eczacıbaşı; Faruk Yalçın Zoo; Field research funding was provided by King Abdullah University of Science and Technology; Fish and Wildlife Compensation Program; Fisheries and Oceans Canada; Florida Fish and Wildlife Conservation Commission, [grant numbers FWC-12164, FWC-14026, FWC-19050]; Fondo Europeo de Desarrollo Regional; Fonds québécois de la recherche nature et technologies; Foundation Segré; Fundação para a Ciência e a Tecnologia (FCT Portugal); Galapagos National Park Directorate research, [grant number PC-41-20]; Gordon and Betty Moore Foundation, [grant number GBMF9881 and GBMF 8072]; Government of Tristan da Cunha; Habitat; Conservation Trust Foundation; Holsworth Wildlife Research Endowment; Institute of Biology of the Southern Seas, Sevastopol, Russia; Instituto de Investigación de Recursos Biológicos Alexander von Humboldt; Instituto Nacional de Pesquisas Espaciais (INPE), Brazil; Israeli Academy of Science's Adams Fellowship; King Family Trust; Labex, CORAIL, France; Liber Ero Fellowship; LIFE (European Union), [grant number LIFE16 NAT/BG/000874]; María de Maeztu Program for Units of Excellence in R&D; Ministry of Science and Innovation, FEDER, SPASIMM,; Spain, [grant number FIS2016–80067-P (AEI/FEDER, UE)]; MOE-Korea, [grant number 2020002990006]; Mohamed bin Zayed Species Conservation Fund; Montreal Space for Life; National Aeronautics and Space Administration (NASA) Earth and Space Science Fellowship Program; National Geographic Society, [grant numbers NGS-82515R-20]; National Natural Science Fund of China; National Oceanic and Atmospheric Administration; National Parks Board, Singapore; National Science and Technology Major Project of China; National Science Foundation, [grant number DEB-1832016]; Natural Environment Research Council of the UK; Natural Sciences and Engineering Research Council of Canada (NSERC), Alliance COVID-19 grant program, [grant numbers ALLRP 550721–20, RGPIN-2014-06229 (year: 2014), RGPIN-2016-05772 (year: 2016)]; Neiser Foundation; Nekton Foundation; Network of Centre of Excellence of Canada: ArcticNet; North Family Foundation; Ocean Tracking Network; Ömer Külahçıoğlu; Oregon State University; Parks Canada Agency (Lake Louise, Yoho, and Kootenay Field Unit); Pew Charitable Trusts; Porsim Kanaf partnership; President's International Fellowship Initiative for postdoctoral researchers Chinese Academy of Sciences, [grant number 2019 PB0143]; Red Sea Research Center; Regional Government of the Azores, [grant number M3.1a/F/025/2015]; Regione Toscana; Rotary Club of Rhinebeck; Save our Seas Foundation; Science & Technology (CSU COAST); Science City Davos, Naturforschende Gesellschaft Davos; Seha İşmen; Sentinelle Nord program from the Canada First Research Excellence Fund; Servizio Foreste e Fauna (Provincia Autonoma di Trento); Sigrid Rausing Trust; Simon Fraser University; Sitka Foundation; Sivil Toplum Geliştirme Merkezi Derneği; South African National Parks (SANParks); South Australian Department for Environment and Water; Southern California Tuna Club (SCTC); Spanish Ministry for the Ecological Transition and the Demographic Challenge; Spanish Ministry of Economy and Competitiveness; Spanish Ministry of Science and Innovation; State of California; Sternlicht Family Foundation; Suna Reyent; Sunshine Coast Regional Council; Tarea Vida, CEMZOC, Universidad de Oriente, Cuba, [grant number 10523, 2020]; Teck Coal; The Hamilton Waterfront Trust; The Ian Potter Foundation, Coastwest, Western Australian State NRM; The Red Sea Development Company; The Wanderlust Fund; The Whitley Fund; Trans-Anatolian Natural Gas Pipeline; Tula Foundation (Hakai Institute); University of Arizona; University of Pisa; US Fish and Wildlife Service; US Geological Survey; Valencian Regional Government; Vermont Center for Ecostudies; Victorian Fisheries Authority; VMRC Fishing License Fund; and Wildlife Warriors Worldwide, With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S