Coral bleaching impacts from back-to-back 2015–2016 thermal anomalies in the remote central Indian Ocean

Studying scleractinian coral bleaching and recovery dynamics in remote, isolated reef systems offers an opportunity to examine impacts of global reef stressors in the absence of local human threats. Reefs in the Chagos Archipelago, central Indian Ocean, suffered severe bleaching and mortality in 2015 following a 7.5 maximum degree heating weeks (DHWs) thermal anomaly, causing a 60% coral cover decrease from 30% cover in 2012 to 12% in April 2016. Mortality was taxon specific, with Porites becoming the dominant coral genus post-bleaching because of an 86% decline in Acropora from 14 to 2% cover. Spatial heterogeneity in Acropora mortality across the Archipelago was significantly negatively correlated with variation in DHWs and with chlorophyll-a concentrations. In 2016, a 17.6 maximum DHWs thermal anomaly caused further damage, with 68% of remaining corals bleaching in May 2016, and coral cover further declining by 29% at Peros Banhos Atoll (northern Chagos Archipelago) from 14% in March 2016 to 10% in April 2017. We therefore document back-to-back coral bleaching and mortality events for two successive years in the remote central Indian Ocean. Our results indicate lower coral mortality in 2016 than 2015 despite a more severe thermal anomaly event in 2016. This could be caused by increased thermal resistance and resilience within corals surviving the 2015 thermal anomaly; however, high bleaching prevalence in 2016 suggests there remained a high sensitivity to bleaching. Similar coral mortality and community change were seen in the Chagos Archipelago following the 1998 global bleaching event, from which recovery took 10 yr. This relatively rapid recovery suggests high reef resiliency and indicates that the Archipelago’s lack of local disturbances will increase the probability that the reefs will again recover over time. However, as the return time between thermal anomaly events becomes shorter, this ability to recover will become increasingly compromised

Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks

Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis

Harnessing Big Data to Support the Conservation and Rehabilitation of Mangrove Forests Globally

Mangrove forests are found on sheltered coastlines in tropical, subtropical, and some warm temperate regions. These forests support unique biodiversity and provide a range of benefits to coastal communities, but as a result of large-scale conversion for aquaculture, agriculture, and urbanization, mangroves are considered increasingly threatened ecosystems. Scientific advances have led to accurate and comprehensive global datasets on mangrove extent, structure, and condition, and these can support evaluation of ecosystem services and stimulate greater conservation and rehabilitation efforts. To increase the utility and uptake of these products, in this Perspective we provide an overview of these recent and forthcoming global datasets and explore the challenges of translating these new analyses into policy action and on the ground conservation. We describe a new platform for visualizing and disseminating these datasets to the global science community, non-governmental organizations, government officials, and rehabilitation practitioners and highlight future directions and collaborations to increase the uptake and impact of largescale mangrove research

Faktor-Faktor Yang Mempengaruhi Motivasi Para Investor Pada Keputusan Pembelian Residensial Properti Di Yogyakarta

Penelitian ini dilakukan untuk mengetahui pengaruh faktorlokasi, keuangan, fitur rumah, lingkungan sekitar terhadap motivasi konsumen terhadap pembelian residensial properti di Yogyakarta. Teori yang digunakan pada penelitian ini adalah teori investasi dari Sharpe et al 2006, teori motivasi konsumen dari buku Kotler 2016 dan teori pendukung lainnya. Pada penelitian ini disebarkan sebanyak 150 kuesioner. Penelitian ini menggunakan alat analisis regresi linear berganda dengan program SPSS 19for windows. Hasil penelitian ini didapatkan faktorlokasi, keuangan, fitur rumah, lingkungan sekitar berpengaruh positif terhadap motivasi konsumen terhadap pembelian residensial properti di Yogyakarta

Harnessing big data to support the conservation and rehabilitation of mangrove forests globally

Mangrove forests are found on sheltered coastlines in tropical, subtropical, and some warm temperate regions. These forests support unique biodiversity and provide a range of benefits to coastal communities, but as a result of large-scale conversion for aquaculture, agriculture, and urbanization, mangroves are considered increasingly threatened ecosystems. Scientific advances have led to accurate and comprehensive global datasets on mangrove extent, structure, and condition, and these can support evaluation of ecosystem services and stimulate greater conservation and rehabilitation efforts. To increase the utility and uptake of these products, in this Perspective we provide an overview of these recent and forthcoming global datasets and explore the challenges of translating these new analyses into policy action and on-the-ground conservation. We describe a new platform for visualizing and disseminating these datasets to the global science community, non-governmental organizations, government officials, and rehabilitation practitioners and highlight future directions and collaborations to increase the uptake and impact of large-scale mangrove research. This Perspective reviews the role of global-scale research in stimulating policy action and on-the-ground conservation for mangrove ecosystems. We outline the current state of knowledge in terms of global analyses and examine the challenge of translating this research in action

Aggregating behaviour in invasive Caribbean lionfish is driven by habitat complexity

Caribbean lionfish (Pterois spp.) are considered the most heavily impacting invasive marine vertebrate ever recorded. However, current management is largely inadequate, relying on opportunistic culling by recreational SCUBA divers. Culling efficiency could be greatly improved by exploiting natural aggregations, but to date this behaviour has only been recorded anecdotally, and the drivers are unknown. We found aggregations to be common in situ, but detected no conspecific attraction through visual or olfactory cues in laboratory experiments. Aggregating individuals were on average larger, but showed no further differences in morphology or life history. However, using visual assessments and 3D modelling we show lionfish prefer broad-scale, but avoid fine-scale, habitat complexity. We therefore suggest that lionfish aggregations are coincidental based on individuals’ mutual attraction to similar reef structure to maximise hunting efficiency. Using this knowledge, artificial aggregation devices might be developed to concentrate lionfish densities and thus improve culling efficiency

Limited Progress in Improving Gender and Geographic Representation in Coral Reef Science

Despite increasing recognition of the need for more diverse and equitable representation in the sciences, it is unclear whether measurable progress has been made. Here, we examine trends in authorship in coral reef science from 1,677 articles published over the past 16 years (2003–2018) and find that while representation of authors that are women (from 18 to 33%) and from non-OECD nations (from 4 to 13%) have increased over time, progress is slow in achieving more equitable representation. For example, at the current rate, it would take over two decades for female representation to reach 50%. Given that there are more coral reef non-OECD countries, at the current rate, truly equitable representation of non-OECD countries would take even longer. OECD nations also continue to dominate authorship contributions in coral reef science (89%), in research conducted in both OECD (63%) and non-OECD nations (68%). We identify systemic issues that remain prevalent in coral reef science (i.e., parachute science, gender bias) that likely contribute to observed trends. We provide recommendations to address systemic biases in research to foster a more inclusive global science community. Adoption of these recommendations will lead to more creative, innovative, and impactful scientific approaches urgently needed for coral reefs and contribute to environmental justice efforts.We acknowledge the contributions of the many unrecognized and undervalued individuals in coral reef research whose efforts have made it possible for the field to progress. These scientists have collected data, translated across languages, coordinated field work, welcomed foreign visitors to their countries, shared ideas, trained and mentored students, become friends, inspired, and built the foundation for the discipline we know today. We acknowledge the work of all coral reef scientists who continue day after day to pursue equity, inclusion, and justice in the field and for their colleagues and themselves.Ye

Reef Fish Community Biomass and Trophic Structure Changes across Shallow to Upper-Mesophotic Reefs in the Mesoamerican Barrier Reef, Caribbean

Mesophotic coral ecosystems (MCEs; reefs 30-150m depth) are of increased research interest because of their potential role as depth refuges from many shallow reef threats. Yet few studies have identified patterns in fish species composition and trophic group structure between MCEs and their shallow counterparts. Here we explore reef fish species and biomass distributions across shallow to upper-MCE Caribbean reef gradients (5-40m) around Utila, Honduras, using a diver-operated stereo-video system. Broadly, we found reef fish species richness, abundance and biomass declining with depth. At the trophic group level we identified declines in herbivores (both total and relative community biomass) with depth, mostly driven by declines in parrotfish (Scaridae). Piscivores increased as a proportion of the community with increased depth while, in contrast to previous studies, we found no change in relative planktivorous reef fish biomass across the depth gradient. In addition, we also found evidence of ontogenetic migrations in the blue tang (Acanthurus coeruleus), striped parrotfish (Scarus iserti), blue chromis (Chromis cyanea), creole wrasse (Clepticus parrae), bluehead wrasse (Thalassoma bifasciatum) and yellowtail snapper (Ocyurus chrysurus), with a higher proportion of larger individuals at mesophotic and near-mesophotic depths than on shallow reefs. Our results highlight the importance of using biomass measures when considering fish community changes across depth gradients, with biomass generating different results to simple abundance counts