The spatial analysis of biological interactions:Morphological variation responding to the co-occurrence of competitors and resources

By sharing geographic space, species are forced to interact with one another and the contribution of this process to evolutionary and ecological patterns of individual species is not fully understood. At the same time, species turnover makes that species composition varies from one area to another, so the analysis of biological interaction cannot be uncoupled from the spatial context. This is particularly important for clades that show high degree of specialization such as hummingbirds, where any variation in biotic pressures might lead to changes in morphology. Here, we describe the influence of biological interactions on the morphology of Hylocharis leucotis by simultaneously considering potential competition and diet resources. We characterized the extent of local potential competition and local available floral resources by correlating two measurements of hummingbird diversity, floral resources and the size of morphological space of H. leucotis along its geographic distribution. We found that H. leucotis shows an important morphological variability across its range and two groups can be recognized. Surprisingly, morphological variation is not always linked to local hummingbird richness or the phylogenetic similarity of. Only in the southern part of its distribution, H. leucotis is morphologically more variable in those communities where it coexist with closely related hummingbird species. We also found that morphological variation in H. leucotis is independent from the availability of floral resources. Our results suggest that abiotic factors might be responsible for morphological differences across populations in Hylocharis leucotis being biological interactions of minor importance.</p

Understanding citizen science and environmental monitoring: final report on behalf of UK Environmental Observation Framework

Citizen science can broadly be defined as the involvement of volunteers in science. Over the past decade there has been a rapid increase in the number of citizen science initiatives. The breadth of environmental-based citizen science is immense. Citizen scientists have surveyed for and monitored a broad range of taxa, and also contributed data on weather and habitats reflecting an increase in engagement with a diverse range of observational science. Citizen science has taken many varied approaches from citizen-led (co-created) projects with local community groups to, more commonly, scientist-led mass participation initiatives that are open to all sectors of society. Citizen science provides an indispensable means of combining environmental research with environmental education and wildlife recording. Here we provide a synthesis of extant citizen science projects using a novel cross-cutting approach to objectively assess understanding of citizen science and environmental monitoring including: 1. Brief overview of knowledge on the motivations of volunteers. 2. Semi-systematic review of environmental citizen science projects in order to understand the variety of extant citizen science projects. 3. Collation of detailed case studies on a selection of projects to complement the semi-systematic review. 4. Structured interviews with users of citizen science and environmental monitoring data focussing on policy, in order to more fully understand how citizen science can fit into policy needs. 5. Review of technology in citizen science and an exploration of future opportunities

Efficient Algorithms for Robust Spatiotemporal Data Analysis

Many large-scale data analysis applications involve data that can vary over both time and space. Often the primary goal of analyzing spatiotemporal data is identifying trends, movements, and sudden changes with respect to time, location, or both. This can include a variety of applications in economics (housing prices, unemployment, job movement, etc), city planning (traffic, power consumption, resource allocation, etc), and ecology (migration patterns, species variety, habitat change, etc). Like many domains, one of the major challenges of spatiotemporal data is dealing with noise and missing or untrustworthy observations. These uncertainties make it difficult to ascertain the distinct roles that changes in time and location have on the data. To this end, I have developed two different approaches for dealing with data uncertainty in different spatiotemporal applications. The first approach, dubbed the Quantile Scan algorithm, makes use of quantile regression to more accurately identify anomalous regions in the data. The flexibility of this framework allows ‘anomalies’ to be defined with respect to any quantile of interest. I develop a version of the Quantile Scan algorithm for analyzing spatial, and spatiotemporal data. The second approach is a unique variation of Collective Graphical Models (CGMs) to incorporate multiple views of the data. This multiview model learns and leverages shared information between the views to better compensate for missing observations. Both the Quantile Scan and Multiview CGM algorithms improve accuracy and robustness on noisy data without sacrificing runtime. The speed and accuracy of these models is demonstrated on a variety of synthetic and real-world datasets, compared against existing algorithms