Biodiversity data mining from Argus-eyed citizens: the first illegal introduction record of Lepomis macrochirus macrochirus Rafinesque, 1819 in Japan based on Twitter information

The pillars of the sea: strategies to achieve successful marine citizen science programs in the Mediterranean area

Marine ecosystems are facing a dramatic loss of biodiversity worldwide, together with a widespread collapse of habitats and their functionality. In this context, Marine Citizen Science (MCS) can be a powerful tool to monitor these changes over time. The flowering of very well-structured international projects is strengthening the scientific credibility of MCS data, especially when data are collected after specifically designed training programs and shared in public user-friendly repositories. Here we present a new perspective on the use of MCS in the Mediterranean area, along with the main benefits for the stakeholders (i.e., diving centers, trainers, and policymakers) and the users (i.e., divers), resumed in three pillars: Pillar I - MCS as a tool for the site valorization; Pillar II - MCS as a new career opportunity for graduated students; Pillar III - MCS as a business opportunity for diving centers. In the frame of the Quintuple Helix Approach, for which there is a strong need of a socioecological transition of the society and economy, we show how MCS can be a win-win-win solution for all the actors involved, providing the vision for new and highly qualified job and business opportunities for the diving sector.

Deep learning increases the availability of organism photographs taken by citizens in citizen science programs

Citizen science programs using organism photographs have become popular, but there are two problems related to photographs. One problem is the low quality of photographs. It is laborious to identify species in photographs taken outdoors because they are out of focus, partially invisible, or under different lighting conditions. The other is difficulty for non-experts to identify species. Organisms usually have interspecific similarity and intraspecific variation, which hinder species identification by non-experts. Deep learning solves these problems and increases the availability of organism photographs. We trained a deep convolutional neural network, Xception, to identify bee species using various quality of bee photographs that were taken by citizens. These bees belonged to two honey bee species and 10 bumble bee species with interspecific similarity and intraspecific variation. We investigated the accuracy of species identification by biologists and deep learning. The accuracy of species identification by Xception (83.4%) was much higher than that of biologists (53.7%). When we grouped bee photographs by different colors resulting from intraspecific variation in addition to species, the accuracy of species identification by Xception increased to 84.7%. The collaboration with deep learning and experts will increase the reliability of species identification and their use for scientific researches.

Water pre-filtration methods to improve environmental DNA detection by real-time PCR and metabarcoding

Environmental DNA (eDNA) analysis is a novel approach for biomonitoring and has been mostly used in clear water. It is difficult to detect eDNA in turbid water as filter clogging occurs, and environmental samples contain various substances that inhibit the polymerase chain reaction (PCR) and affect the accuracy of eDNA analysis. Therefore, we applied a pre-filtration method to better detect the fish species (particularly pale chub, Opsariichthys platypus) present in a water body by measuring eDNA in environmental samples containing PCR inhibitors. Upon conducting 12S rRNA metabarcoding analysis (MiFish), we found that pre-filtration did not affect the number or identities of fish species detected in our samples, but pre-filtration through pore sizes resulted in significantly reduced variance among replicate samples. Additionally, PCR amplification was improved by the pre-filtration of environmental samples containing PCR inhibitors such as humic substances. Although this study may appear to be a conservative and ancillary experiment, pre-filtration is a simple technique that can not only improve the physical properties of water, such as turbidity, but also the quality of eDNA biomonitoring.

Citizen science yields first records of Hippocampus japapigu and Hippocampus denise (Syngnathidae) from Taiwan: A hotspot for pygmy seahorse diversity

Relatively very little is known about pygmy seahorses, and even basic information regarding their distributions is largely inconsistent and often based on unofficial reports. However, monitoring marine diversity, particularly for small and cryptic species, such as pygmy seahorses, can be both costly and time consuming. In such cases, the use of citizen science can offer an effective tool for addressing knowledge gaps caused by a lack of biodiversity-related data. Scuba divers and underwater photographers were engaged through social media in order to investigate pygmy seahorse diversity in Taiwan. Using this approach five species of pygmy seahorses were identified, including two new records for Taiwan: Hippocampusdenise and Hippocampusjapapigu, the latter of which is the first record of the species from outside of Japan. These new records mark Taiwan as one of the world’s pygmy seahorse diversity hotspots, matching that of Japan and Indonesia, as well as demonstrating the value of citizen science for marine biodiversity monitoring, particularly for small cryptic species.

Sampling alien species inside and outside protected areas: Does it matter?

Data of alien species presences are generally more readily available in protected than non-protected areas due to higher sampling efforts inside protected areas. Are the results and conclusions based on analyses of data collected in protected areas representative of wider non-protected regions? We address this question by analysing some recently published data of alien plants in Greece. Mixed effects models were used with alien species presences in 8.25×8.25km cells as dependent variable and the percentage of protected area, as well as the agricultural and artificial land cover types richness (as indicators of human presence) as independent variables. In addition, the spatial cross-correlation between the percentage of protected area and alien species richness was examined across scales. Results indicated that the percentage of protected area per cell is a poor predictor of alien species richness. Spatial analysis indicated that cells with higher percentage of protected areas have slightly less alien species than cells with lower percentage of protected areas. This result is likely to be driven by the overall negative correlation between habitat protection and anthropogenic activities. Thus, the conclusions deduced by data deriving from protected areas are likely to hold true for patterns of alien species in non-protected areas when the human pressures are accounted for.