Improving citizen science data for long-term monitoring of plant species in the Netherlands

Biodiversity and Conservation - In 2012, a new volunteer-based recording scheme for vascular plants was launched in the Netherlands. Its purpose is to track the changes in the number of occupied...     

Modeling the distribution of a wide‐ranging invasive species using the sampling efforts of expert and citizen scientists

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Understanding sampling and taxonomic biases recorded by citizen scientists

Projects involving citizen scientists have greatly increased over the last decade and understanding errors associated with such projects has been identified as an important step. NatureWatch NZ is a biodiversity recording system accessible to members of the public. The “NZ wasps, ants, bees and parasitoids (Hymenoptera) project” was initiated within NatureWatch NZ in December 2012, and comparisons were analysed between these records and the known Hymenoptera fauna of the New Zealand region. Over the course of 1 year 25 members contributed 360 records from 186 taxa, including the discovery of several introduced species new to New Zealand. There was a strong geographical bias to the records, with the majority being based around the major cities. Aculeates (stinging wasps) were significantly over-represented in the NatureWatch records. Only half (55 %) of taxa were identified to species level, with a further 28 % at genus level, and 17 % identified above genus level (family, order). Furthermore, the majority (65 %) of taxa were recorded only once, and only a few taxa were recorded >5 times (top records were “Ichneumonidae”, “Hymenoptera”, Anthidium manicatum, and Apis mellifera). It is probable that these same biases also exist for many other taxonomic groups in projects operated by citizen scientists lacking set protocols. Caution should be exercised on the subsequent use, compilation, and analysis of citizen science, especially without prior examination of records and potential biases.     

Distribution models for koalas in South Australia using citizen science‐collected data

The koala (Phascolarctos cinereus) occurs in the eucalypt forests of eastern and southern Australia and is currently threatened by habitat fragmentation, climate change, sexually transmitted diseases, and low genetic variability throughout most of its range. Using data collected during the Great Koala Count (a 1-day citizen science project in the state of South Australia), we developed generalized linear mixed-effects models to predict habitat suitability across South Australia accounting for potential errors associated with the dataset. We derived spatial environmental predictors for vegetation (based on dominant species of Eucalyptus or other vegetation), topographic water features, rain, elevation, and temperature range. We also included predictors accounting for human disturbance based on transport infrastructure (sealed and unsealed roads). We generated random pseudo-absences to account for the high prevalence bias typical of citizen-collected data. We accounted for biased sampling effort along sealed and unsealed roads by including an offset for distance to transport infrastructures. The model with the highest statistical support (wAIC_c ∼ 1) included all variables except rain, which was highly correlated with elevation. The same model also explained the highest deviance (61.6%), resulted in high R²(m) (76.4) and R²(c) (81.0), and had a good performance according to Cohen''s κ (0.46). Cross-validation error was low (∼ 0.1). Temperature range, elevation, and rain were the best predictors of koala occurrence. Our models predict high habitat suitability in Kangaroo Island, along the Mount Lofty Ranges, and at the tips of the Eyre, Yorke and Fleurieu Peninsulas. In the highest-density region (5576 km²) of the Adelaide–Mount Lofty Ranges, a density–suitability relationship predicts a population of 113,704 (95% confidence interval: 27,685–199,723; average density = 5.0–35.8 km⁻²). We demonstrate the power of citizen science data for predicting species'' distributions provided that the statistical approaches applied account for some uncertainties and potential biases. A future improvement to citizen science surveys to provide better data on search effort is that smartphone apps could be activated at the start of the search. The results of our models provide preliminary ranges of habitat suitability and population size for a species for which previous data have been difficult or impossible to gather otherwise.     

Marine invasive species: validation of citizen science and implications for national monitoring networks

Approximately 1,000 volunteers assessed the presence of invasive (Carcinus maenas and Hemigrapsus sanguineus) and native crabs within the intertidal zone of seven coastal states of the US, from New Jersey to Maine. Identification of crab species and determination of the gender of the observed crabs was documented at all 52 sites across a 725-km coastal transect. Using quantitative measures of accuracy of data collected by citizen scientists, a significant predictor of a volunteer’s ability was determined and eligibility criteria were set. Students in grade three and seven had the ability to differentiate between species of crabs with over 80% and 95% accuracy, respectively. Determination of gender of the crabs was more challenging and accuracy exceeded 80% for seventh grade students, while 95% accuracy was found for students with at least 2 years of university education. We used the data collected by citizen scientists to create a large-scale standardized database of the distribution and abundance of the native and invasive crabs. Hemigrapsus sanguineus dominated the rocky intertidal zone from Sandy Hook, New Jersey to Boston Harbor, Massachusetts while C. maenas dominated the northern extent of the sampled coastline. A citizen scientist of this monitoring network detected a range expansion of H. sanguineus. We identified obstacles to creating a national monitoring network and proposed recommendations that addressed these issues.     

Mosquito traps for urban surveillance: collection efficacy and potential for use by citizen scientists

Mosquito‐borne diseases are a pervasive public health problem on a global scale, and effective management of them requires well‐designed surveillance programs for both vectors and pathogens. Mosquito traps are a common component of such programs, and their reach can be expanded by engaging citizen scientists. In this study in a southern Australian city, we compared the mosquito collection efficacy of two types of traps and assessed their suitability for use in citizen science programs. BG Sentinels and BG Gravid Aedes Trap (BG‐GAT) traps both collected Aedes and Culex species in similar proportions, albeit with the former collecting approximately nine times as many mosquitoes. However, BG Sentinels have a greater per unit cost than BG‐GATs and are restricted to deployment near power outlets. Importantly, despite being devised for collection of Aedes (Stegomyia) dengue vectors (such as Aedes aegypti), both traps can be effectively used in temperate climates for collection of a range of mosquito species. These traps could conceivably be used in citizen science programs to enhance the reach of surveillance at reduced cost.     

Evaluating citizen science data for forecasting species responses to national forest management

The extensive spatial and temporal coverage of many citizen science datasets (CSD) makes them appealing for use in species distribution modeling and forecasting. However, a frequent limitation is the inability to validate results. Here, we aim to assess the reliability of CSD for forecasting species occurrence in response to national forest management projections (representing 160,366 km²) by comparison against forecasts from a model based on systematically collected colonization–extinction data. We fitted species distribution models using citizen science observations of an old‐forest indicator fungus Phellinus ferrugineofuscus. We applied five modeling approaches (generalized linear model, Poisson process model, Bayesian occupancy model, and two MaxEnt models). Models were used to forecast changes in occurrence in response to national forest management for 2020‐2110. Forecasts of species occurrence from models based on CSD were congruent with forecasts made using the colonization–extinction model based on systematically collected data, although different modeling methods indicated different levels of change. All models projected increased occurrence in set‐aside forest from 2020 to 2110: the projected increase varied between 125% and 195% among models based on CSD, in comparison with an increase of 129% according to the colonization–extinction model. All but one model based on CSD projected a decline in production forest, which varied between 11% and 49%, compared to a decline of 41% using the colonization–extinction model. All models thus highlighted the importance of protected old forest for P. ferrugineofuscus persistence. We conclude that models based on CSD can reproduce forecasts from models based on systematically collected colonization–extinction data and so lead to the same forest management conclusions. Our results show that the use of a suite of models allows CSD to be reliably applied to land management and conservation decision making, demonstrating that widely available CSD can be a valuable forecasting resource.     

Improving melanoma classification by integrating genetic and morphologic features

Background

In melanoma, morphology-based classification systems have not been able to provide relevant information for selecting treatments for patients whose tumors have metastasized. The recent identification of causative genetic alterations has revealed mutations in signaling pathways that offer targets for therapy. Identifying morphologic surrogates that can identify patients whose tumors express such alterations (or functionally equivalent alterations) would be clinically useful for therapy stratification and for retrospective analysis of clinical trial data.

Methodology/Principal Findings

We defined and assessed a panel of histomorphologic measures and correlated them with the mutation status of the oncogenes BRAF and NRAS in a cohort of 302 archival tissues of primary cutaneous melanomas from an academic comprehensive cancer center. Melanomas with BRAF mutations showed distinct morphological features such as increased upward migration and nest formation of intraepidermal melanocytes, thickening of the involved epidermis, and sharper demarcation to the surrounding skin; and they had larger, rounder, and more pigmented tumor cells (all p-values below 0.0001). By contrast, melanomas with NRAS mutations could not be distinguished based on these morphological features. Using simple combinations of features, BRAF mutation status could be predicted with up to 90.8% accuracy in the entire cohort as well as within the categories of the current World Health Organization (WHO) classification. Among the variables routinely recorded in cancer registries, we identified age < 55 y as the single most predictive factor of BRAF mutation in our cohort. Using age < 55 y as a surrogate for BRAF mutation in an independent cohort of 4,785 patients of the Southern German Tumor Registry, we found a significant survival benefit (p < 0.0001) for patients who, based on their age, were predicted to have BRAF mutant melanomas in 69% of the cases. This group also showed a different pattern of metastasis, more frequently involving regional lymph nodes, compared to the patients predicted to have no BRAF mutation and who more frequently displayed satellite, in-transit metastasis, and visceral metastasis (p < 0.0001).

Conclusions

Refined morphological classification of primary melanomas can be used to improve existing melanoma classifications by forming subgroups that are genetically more homogeneous and likely to differ in important clinical variables such as outcome and pattern of metastasis. We expect this information to improve classification and facilitate stratification for therapy as well as retrospective analysis of existing trial data.     

Correcting for bias in distribution modelling for rare species using citizen science data

Aim

To improve the accuracy of inferences on habitat associations and distribution patterns of rare species by combining machine‐learning, spatial filtering and resampling to address class imbalance and spatial bias of large volumes of citizen science data.

Innovation

Modelling rare species’ distributions is a pressing challenge for conservation and applied research. Often, a large number of surveys are required before enough detections occur to model distributions of rare species accurately, resulting in a data set with a high proportion of non‐detections (i.e. class imbalance). Citizen science data can provide a cost‐effective source of surveys but likely suffer from class imbalance. Citizen science data also suffer from spatial bias, likely from preferential sampling. To correct for class imbalance and spatial bias, we used spatial filtering to under‐sample the majority class (non‐detection) while maintaining all of the limited information from the minority class (detection). We investigated the use of spatial under‐sampling with randomForest models and compared it to common approaches used for imbalanced data, the synthetic minority oversampling technique (SMOTE), weighted random forest and balanced random forest models. Model accuracy was assessed using kappa, Brier score and AUC. We demonstrate the method by evaluating habitat associations and seasonal distribution patterns using citizen science data for a rare species, the tricoloured blackbird (Agelaius tricolor).

Main Conclusions

Spatial under‐sampling increased the accuracy of each model and outperformed the approach typically used to direct under‐sampling in the SMOTE algorithm. Our approach is the first to characterize winter distribution and movement of tricoloured blackbirds. Our results show that tricoloured blackbirds are positively associated with grassland, pasture and wetland habitats, and negatively associated with high elevations or evergreen forests during both winter and breeding seasons. The seasonal differences in distribution indicate that individuals move to the coast during the winter, as suggested by historical accounts.

     

The how and why of societal publications for citizen science projects and scientists

In the scientific community, the importance of communication to society is often underestimated. Scientists and scientific organisations often lack the skills to organise such communication effectively. The Dutch citizen science phenology network Nature’s Calendar has been successful in communicating to the general public via numerous newspaper articles, television appearances, presentations, websites and social media. We refer to these publications as societal publications. Due to active communication to mass media, we frequently reach millions of people. This communication helped us to involve thousands of volunteers in recording the timing of phenological events like the start of flowering, leaf unfolding and bird migration, but also several health-related events like hay fever symptoms and tick bites. In this paper, we analyse and present our experiences with the Nature’s Calendar project regarding societal publications. Based on this analysis, we explain the importance of societal publications for citizen science projects and scientists in general, and we show how scientists can increase the newsworthiness of scientific information and what factors and activities can increase the chances of media paying attention to this news. We show that societal publications help phenological networks by facilitating the recruitment, retention and instruction of observers. Furthermore, they stimulate the generation of new ideas and partners that lead to an increase in knowledge, awareness and behavioural change of the general public or specific stakeholders. They make projects, and scientists involved, better known to the public and increase their credibility and authority. Societal publications can catalyse the production of new publications, thereby enforcing the previous mentioned points.     

The fall migration flyways of monarch butterflies in eastern North America revealed by citizen scientists

The migration of monarch butterflies (Danaus plexippus) from Canada and the United States to overwintering sites in Mexico is one of the world’s most amazing biological phenomena, although recent threats make it imperative that the resources needed by migrating monarchs be conserved. The most important first step in preserving migration resources—determining the migration flyways—is also the most challenging because of the large-scale nature of the migration. Prior attempts to determine the flyways using mark-recapture techniques with wing tags gave some clues, but this important information has never been fully obtainable until now. In 2005 the citizen-science program, Journey North, initiated a project that asked participants to record sightings of overnight roosts of monarchs during their fall migration, and this project now provides an ideal way to illustrate the flyways used by monarchs on their way to Mexico, with the assumption that roost locations indicate migration routes. We used 3 years of this data to elucidate the flyways on a continent-wide scale, that revealed two distinct flyways, but only one appears to lead directly to Mexico. This main, ‘central’ flyway begins in the American Midwest states and southern Ontario, then continues south-southwest through the states of Kansas, Missouri, Oklahoma and Arkansas, and finally passes through Texas and northern Mexico. These data also highlighted a separate, smaller flyway along the eastern and coastal states, but there was a noticeable lack of roost sightings in this flyway at lower latitudes. Since there are few recoveries of marked monarchs in Mexico originating from coastal areas, we compared the timing of roost formation in this ‘eastern’ flyway with the main, central flyway. Roosts in the eastern flyway lagged behind the central roosts in timing, suggesting that monarchs traveling in this flyway have a reduced chance of making it to the Mexico wintering site. Combined, our evidence indicates that locations in the central flyway should be considered priority areas for conserving migration resources.     

Object-based classification of land cover and tree species by integrating airborne LiDAR and high spatial resolution imagery data

We evaluated the effectiveness of integrating discrete return light detection and ranging (LiDAR) data with high spatial resolution near-infrared digital imagery for object-based classification of land cover types and dominant tree species. In particular we adopted LiDAR ratio features based on pulse attributes that have not been used in past studies. Object-based classifications were performed first on land cover types, and subsequently on dominant tree species within the area classified as trees. In each classification stage, two different data combinations were examined: LiDAR data integrated with digital imagery or digital imagery only. We created basic image objects and calculated a number of spectral, textural, and LiDAR-based features for each image object. Decision tree analysis was performed and important features were investigated in each classification. In the land cover classification, the overall accuracy was improved to 0.975 when using the object-based method and integrating LiDAR data. The mean height value derived from the LiDAR data was effective in separating “trees” and “lawn” objects having different height. As for the tree species classification, the overall accuracy was also improved by object-based classification with LiDAR data although it remained up to 0.484 because spectral and textural signatures were similar among tree species. We revealed that the LiDAR ratio features associated with laser penetration proportion were important in the object-based classification as they can distinguish tree species having different canopy density. We concluded that integrating LiDAR data was effective in the object-based classifications of land cover and dominant tree species.     

Mapping an invasive bryophyte species using hyperspectral remote sensing data

Reliable distribution maps are crucial for the management of invasive plant species. An alternative to traditional field surveys is the use of remote sensing data, which allows coverage of large areas. However, most remote sensing studies on invasive plant species focus on mapping large stands of easily detectable study species. In this study, we used hyperspectral remote sensing data in combination with field data to derive a distribution map of an invasive bryophyte species, Campylopus introflexus, on the island of Sylt in Northern Germany. We collected plant cover data on 57 plots to calibrate the model and presence/absence data of C. introflexus on another 150 plots for independent validation. We simultaneously acquired airborne hyperspectral (APEX) images during summer 2014, providing 285 spectral bands. We used a Maxent modelling approach to map the distribution of C. introflexus. Although C. introflexus is a small and inconspicuous species, we were able to map its distribution with an overall accuracy of 75 %. Reducing the sampling effort from 57 to 7 plots, our models performed fairly well until sampling effort dropped below 12 plots. The model predicts that C. introflexus is present in about one quarter of the pixels in our study area. The highest percentage of C. introflexus is predicted in the dune grassland. Our findings suggest that hyperspectral remote sensing data have the potential to provide reliable information about the degree of bryophyte invasion, and thus provide an alternative to traditional field mapping approaches over large areas.     

Mayan diver-fishers as citizen scientists: detection and monitoring of the invasive red lionfish in the Parque Nacional Arrecife Alacranes,southern Gulf of Mexico

Biological invasions research has increasingly incorporated the participation of citizen scientists to collect data for monitoring and management purposes. This study outlines collaborative efforts with Mayan lobster diver-fishers, who participated voluntarily as citizen scientists in surveys, to detect and monitor the invasive red lionfish, Pterois volitans, in a marine protected area off the northern Yucatan Peninsula, Mexico. We engaged these fishers by building local capacity and awareness of the threats of the lionfish invasion on the fishery resources of the area. During the lobster fishing season (July 2010–February 2011), 30 fishers collected 248 red lionfish (9–29 cm TL) and recorded ancillary data (day, month, depth, and approximate coordinates of capture site). This collaboration not only allowed the first lionfish detection in the Parque Nacional Arrecife Alacranes in 2010, but the volunteer-based monitoring effort revealed that the invasion reached levels from intermediate to advanced, with more than 200 specimens captured in less than a year. Our results support the notion that engaging citizen scientists can enhance research, reduce costs, and improve the possibility of a long term monitoring survey.     

Prediction of drug combinations by integrating molecular and pharmacological data

Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed approach can help to limit the search space of drug combinations and provide a new way to effectively utilize existing drugs for new purposes.