Development of a Seafloor Community Classification for the New Zealand Region Using a Gradient Forest Approach

To support ongoing marine spatial planning in New Zealand, a numerical environmental classification using Gradient Forest models was developed using a broad suite of biotic and high-resolution environmental predictor variables. Gradient Forest modeling uses species distribution data to control the selection, weighting and transformation of environmental predictors to maximise their correlation with species compositional turnover. A total of 630,997 records (39,766 unique locations) of 1,716 taxa living on or near the seafloor were used to inform the transformation of 20 gridded environmental variables to represent spatial patterns of compositional turnover in four biotic groups and the overall seafloor community. Compositional turnover of the overall community was classified using a hierarchical procedure to define groups at different levels of classification detail. The 75-group level classification was assessed as representing the highest number of groups that captured the majority of the variation across the New Zealand marine environment. We refer to this classification as the New Zealand “Seafloor Community Classification” (SCC). Associated uncertainty estimates of compositional turnover for each of the biotic groups and overall community were also produced, and an added measure of uncertainty – coverage of the environmental space – was developed to further highlight geographic areas where predictions may be less certain owing to low sampling effort. Environmental differences among the deep-water New Zealand SCC groups were relatively muted, but greater environmental differences were evident among groups at intermediate depths in line with well-defined oceanographic patterns observed in New Zealand’s oceans. Environmental differences became even more pronounced at shallow depths, where variation in more localised environmental conditions such as productivity, seafloor topography, seabed disturbance and tidal currents were important differentiating factors. Environmental similarities in New Zealand SCC groups were mirrored by their biological compositions. The New Zealand SCC is a significant advance on previous numerical classifications and includes a substantially wider range of biological and environmental data than has been attempted previously. The classification is critically appraised and considerations for use in spatial management are discussed.

Superpixel-Based Regional-Scale Grassland Community Classification Using Genetic Programming with Sentinel-1 SAR and Sentinel-2 Multispectral Images

Grasslands are one of the most important terrestrial ecosystems on the planet and have significant economic and ecological value. Accurate and rapid discrimination of grassland communities is critical to the conservation and utilization of grassland resources. Previous studies that explored grassland communities were mainly based on field surveys or airborne hyperspectral and high-resolution imagery. Limited by workload and cost, these methods are typically suitable for small areas. Spaceborne mid-resolution RS images (e.g., Sentinel, Landsat) have been widely used for large-scale vegetation observations owing to their large swath width. However, there still keep challenges in accurately distinguishing between different grassland communities using these images because of the strong spectral similarity of different communities and the suboptimal performance of models used for classification. To address this issue, this paper proposed a superpixel-based grassland community classification method using Genetic Programming (GP)-optimized classification model with Sentinel-2 multispectral bands, their derived vegetation indices (VIs) and textural features, and Sentinel-1 Synthetic Aperture Radar (SAR) bands and the derived textural features. The proposed method was evaluated in the Siziwang grassland of China. Our results showed that the addition of VIs and textures, as well as the use of GP-optimized classification models, can significantly contribute to distinguishing grassland communities, and the proposed approach classified the seven communities in Siziwang grassland with an overall accuracy of 84.21% and a kappa coefficient of 0.81. We concluded that the classification method proposed in this paper is capable of distinguishing grassland communities with high accuracy at a regional scale.

Vegetation ecology with anthropic drivers and consequences

Current research on vegetation makes a difference in people’s lives. Plant community classification is a backbone of land management, plant communities are changing in response to anthropogenic drivers, and the processes of change have impacts on ecosystem services. In the following progress report, we summarize the status of classification and recent research on vegetation responses to pollution, especially nitrogen deposition, invasive species, climate change, and land use and direct exploitation. Two areas with human feedbacks are underscored: fire ecology and urban ecology. Prominent questions at the current research frontier are highlighted with attention to new perspectives.

Fusion of GF and MODIS Data for Regional-Scale Grassland Community Classification with EVI2 Time-Series and Phenological Features

Satellite-borne multispectral data are suitable for regional-scale grassland community classification owing to comprehensive coverage. However, the spectral similarity of different communities makes it challenging to distinguish them based on a single multispectral data. To address this issue, we proposed a support vector machine (SVM)–based method integrating multispectral data, two-band enhanced vegetation index (EVI2) time-series, and phenological features extracted from Chinese GaoFen (GF)-1/6 satellite with (16 m) spatial and (2 d) temporal resolution. To obtain cloud-free images, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm was employed in this study. By using the algorithm on the coarse cloudless images at the same or similar time as the fine images with cloud cover, the cloudless fine images were obtained, and the cloudless EVI2 time-series and phenological features were generated. The developed method was applied to identify grassland communities in Ordos, China. The results show that the Caragana pumila Pojark, Caragana davazamcii Sanchir and Salix schwerinii E. L. Wolf grassland, the Potaninia mongolica Maxim, Ammopiptanthus mongolicus S. H. Cheng and Tetraena mongolica Maxim grassland, the Caryopteris mongholica Bunge and Artemisia ordosica Krasch grassland, the Calligonum mongolicum Turcz grassland, and the Stipa breviflora Griseb and Stipa bungeana Trin grassland are distinguished with an overall accuracy of 87.25%. The results highlight that, compared to multispectral data only, the addition of EVI2 time-series and phenological features improves the classification accuracy by 9.63% and 14.7%, respectively, and even by 27.36% when these two features are combined together, and indicate the advantage of the fine images in this study, compared to 500 m moderate-resolution imaging spectroradiometer (MODIS) data, which are commonly used for grassland classification at regional scale, while using 16 m GF data suggests a 23.96% increase in classification accuracy with the same extracted features. This study indicates that the proposed method is suitable for regional-scale grassland community classification.

1669. Antimicrobial Resistance Patterns of Uropathogenic Escherichia coli: Comparison of Infection Setting and Community Classification

Background This study describes antibiotic resistance rates for Escherichia coli (E. coli) urinary tract infections (UTIs) and assesses differences in resistance patterns based on setting and community classification. Methods A cross-sectional study design was used to analyze antibiotic resistance patterns of E. coli isolates from 12,600 urine cultures processed at a large hospital system in North Carolina from 2016 to 2018. Overall 3-year and annual resistance rates of uropathogenic E. coli to routinely tested first-line antibiotics were determined. Antibiotic resistance rates per 1000 patients were compared based on setting of infection acquisition (hospital vs. community) and community classification (urban vs. rural). T-test and chi-square tests were used to compare extended spectrum beta-lactamases (ESBLs) by demographic factors and setting. Analyses were performed using SAS Version 9.3 (SAS Institute, Cary, NC) at alpha=0.05. Results Three-year resistance rates were highest to ampicillin (42.2%), ampicillin-sulbactam (24.7%), ciprofloxacin (21.8%), trimethoprim-sulfamethoxazole (21.6%), and levoflaxacin (21.4%). Resistance was lowest for amikacin (0.06%), meropenem (0.08%), piperacillin-tazobactam (1.3%), nitrofurantoin (1.4%), and tobramycin (1.8%). Overall resistance rates were significantly higher in hospital- compared to community-acquired UTIs (p< 0.05) with the exception of amikacin, gentamicin, and meropenem. Significant differences in E. coli resistance rates were observed for patients from rural compared to urban counties for these antibiotics: levoflaxacin (233.6 vs. 208.3, p=0.003), ciprofloxacin (239.3 vs. 211.8, p=0.002), and nitrofurantoin (19.6 vs. 12.2, p=0.003). Prevalence of ESBL-producing E. coli was significantly higher among the elderly (p< 0.001), males (p< 0.001), inpatients (p< 0.001), and catheterized patients (p< 0.001). Conclusion Resistance to first-line fluoroquinolones and nitrofurantoin was more prevalent in patients from rural compared to urban areas. Resistance rates and ESBL prevalence were significantly higher for hospital-acquired UTIs. Our findings have important implications for the empirical treatment of UTIs based on geographical area and setting. Disclosures All Authors: No reported disclosures

Foundational biogeography: Vegetation of the Great Smoky Mountains (Ecological Monographs, 26: 1–80, 1956), by Robert H. Whittaker

A seminal paper in biogeography is reviewed. Whittaker’s 1956 paper in Ecological Monographs introduced gradient analysis as a conceptual framework. This approach replaced community classification as the preferred methodology among US ecologists and biogeographers. It later developed into the foundation for species distribution modeling. Although the paper underlies a continuing rift between US and European scientists, both groups recognize its importance for relating ecological processes to geographical patterns.

Analysing political events on Twitter

Recently, political events, such as elections, have raised a lot of discussions on social media networks, in particular, Twitter. This brings new opportunities for social scientists to address social science tasks, such as understanding what communities said or identifying whether a community has an influence on another. However, identifying these communities and extracting what they said from social media data are challenging and non-trivial tasks. We aim to make progress towards understanding 'who' (i.e. communities) said 'what' (i.e. discussed topics) and 'when' (i.e. time) during political events on Twitter. While identifying the 'who' can benefit from Twitter user community classification approaches, 'what' they said and 'when' can be effectively addressed on Twitter by extracting their discussed topics using topic modelling approaches that also account for the importance of time on Twitter. To evaluate the quality of these topics, it is necessary to investigate how coherent these topics are to humans. Accordingly, we propose a series of approaches in this thesis. First, we investigate how to effectively evaluate the coherence of the topics generated using a topic modelling approach. The topic coherence metric evaluates the topical coherence by examining the semantic similarity among words in a topic. We argue that the semantic similarity of words in tweets can be effectively captured by using word embeddings trained using a Twitter background dataset. Through a user study, we demonstrate that our proposed word embedding-based topic coherence metric can assess the coherence of topics like humans [1, 2]. In addition, inspired by the precision at k metric, we propose to evaluate the coherence of a topic model (containing many topics) by averaging the top-ranked topics within the topic model [3]. Our proposed metrics can not only evaluate the coherence of topics and topic models, but also can help users to choose the most coherent topics. Second, we aim to extract topics with a high coherence from Twitter data. Such topics can be easily interpreted by humans and they can assist to examine 'what' has been discussed and 'when'. Indeed, we argue that topics can be discussed in different time periods (see [4]) and therefore can be effectively identified and distinguished by considering their time periods. Hence, we propose an effective time-sensitive topic modelling approach by integrating the time dimension of tweets (i.e. 'when') [5]. We show that the time dimension helps to generate topics with a high coherence. Hence, we argue that 'what' has been discussed and 'when' can be effectively addressed by our proposed time-sensitive topic modelling approach. Next, to identify 'who' participated in the topic discussions, we propose approaches to identify the community affiliations of Twitter users, including automatic ground-truth generation approaches and a user community classification approach. We show that the mentioned hashtags and entities in the users' tweets can indicate which community a Twitter user belongs to. Hence, we argue that they can be used to generate the ground-truth data for classifying users into communities. On the other hand, we argue that different communities favour different topic discussions and their community affiliations can be identified by leveraging the discussed topics. Accordingly, we propose a Topic-Based Naive Bayes (TBNB) classification approach to classify Twitter users based on their words and discussed topics [6]. We demonstrate that our TBNB classifier together with the ground-truth generation approaches can effectively identify the community affiliations of Twitter users. Finally, to show the generalisation of our approaches, we apply our approaches to analyse 3.6 million tweets related to US Election 2016 on Twitter [7]. We show that our TBNB approach can effectively identify the 'who', i.e. classify Twitter users into communities. To investigate 'what' these communities have discussed, we apply our time-sensitive topic modelling approach to extract coherent topics. We finally analyse the community-related topics evaluated and selected using our proposed topic coherence metrics. Overall, we contribute to provide effective approaches to assist social scientists towards analysing political events on Twitter. These approaches include topic coherence metrics, a time-sensitive topic modelling approach and approaches for classifying the community affiliations of Twitter users. Together they make progress to study and understand the connections and dynamics among communities on Twitter. Supervisors : Iadh Ounis, Craig Macdonald, Philip Habel The thesis is available at http://theses.gla.ac.uk/41135/