Corey T. Callaghan

Bio: Corey T. Callaghan is an academic researcher from University of New South Wales. The author has contributed to research in topics: Biodiversity & Citizen science. The author has an hindex of 12, co-authored 55 publications receiving 522 citations. Previous affiliations of Corey T. Callaghan include Martin Luther University of Halle-Wittenberg & Czech University of Life Sciences Prague.

Generalists are the most urban-tolerant of birds: a phylogenetically controlled analysis of ecological and life history traits using a novel continuous measure of bird responses to urbanization

Abstract: Identifying which ecological and life history traits influence a species’ tolerance to urbanization is critical to understanding the trajectory of biodiversity in an increasingly urbanizing world. There is evidence for a wide array of contrasting patterns for single trait associations with urbanization. In a continental‐scale analysis, incorporating 477 species and >5 000 000 bird observations, we developed a novel and scalable methodology that evaluated the ecological and life history traits which most influence a species’ adaptability to persist in urban environments. Specifically, we assigned species‐specific scores based on continuous measures of response to urbanization, using VIIRS night‐time light values (i.e. radiance) as a proxy for urbanization. We identified generalized, phylogenetically controlled patterns: bird species which are generalists (i.e. large niche breadth), with large clutch size, and large residual brain size are among the most urban‐tolerant bird species. Conversely, specialized feeding strategies (i.e. insectivores and granivores) were negatively associated with urbanization. Enhancement and persistence of avian biodiversity in urban environments probably relies on protecting, maintaining and restoring diverse habitats serving a range of life history strategies.

Improving big citizen science data: Moving beyond haphazard sampling.

Abstract: Citizen science is mainstream: millions of people contribute data to a growing array of citizen science projects annually, forming massive datasets that will drive research for years to come. Many citizen science projects implement a “leaderboard” framework, ranking the contributions based on number of records or species, encouraging further participation. But is every data point equally “valuable?” Citizen scientists collect data with distinct spatial and temporal biases, leading to unfortunate gaps and redundancies, which create statistical and informational problems for downstream analyses. Up to this point, the haphazard structure of the data has been seen as an unfortunate but unchangeable aspect of citizen science data. However, we argue here that this issue can actually be addressed: we provide a very simple, tractable framework that could be adapted by broadscale citizen science projects to allow citizen scientists to optimize the marginal value of their efforts, increasing the overall collective knowledge.

Efficacy of eBird data as an aid in conservation planning and monitoring

Abstract: One of the world's largest citizen science projects is eBird, a database that has been used primarily to address questions of bird distributions and abundance over large spatial scales. However, addressing finer-scale questions is also possible, depending on survey coverage and whether assumptions and limitations are matched to the scale of inferences. Our objective was to determine if the eBird database could be used to develop estimates of bird abundance and diversity comparable to those from standardized shorebird surveys. We compared a year of standardized shorebird surveys by trained observers at Snook Islands Natural Area located in Palm Beach County, Florida, to a year of eBird observations from the same site. Total species richness derived from eBird (25 species) was higher than that from standardized surveys (20 species). Similarly, we found the Shannon diversity index calculated from eBird was higher (2.81) than the same index calculated from standardized surveys (2.21; P < 0.001). The higher diversity and species richness may reflect the greater effort of eBird participants (35,289 person-hours) compared to our standard surveys (2126 person-hours). We found only a slight difference in parameter estimates between data obtained from eBird and from standardized surveys. Potential use and value of eBird as a tool for land managers and conservationists may be greater than currently realized, but studies conducted in a wider range of ecosystems and locations are needed to develop generalizations.

Monitoring large and complex wildlife aggregations with drones

Abstract: Recent advances in drone technology have rapidly led to their use for monitoring and managing wildlife populations but a broad and generalised framework for their application to complex wildlife aggregations is still lacking. We present a generalised semi-automated approach where machine learning can map targets of interest in drone imagery, supported by predictive modelling for estimating wildlife aggregation populations. We demonstrated this application on four large spatially complex breeding waterbird colonies on floodplains, ranging from c. 20,000 to c. 250,000 birds, providing estimates of bird nests. Our mapping and modelling approach was applicable to all four colonies, without any modification, effectively dealing with variation in nest size, shape, colour and density and considerable background variation (vegetation, water, sand, soil, etc.). Our semi-automated approach was between three and eight times faster than manually counting nests from imagery at the same level of accuracy. This approach is a significant improvement for monitoring large and complex aggregations of wildlife, offering an innovative solution where ground counts are costly, difficult or not possible. Our framework requires minimal technical ability, is open-source (Google Earth Engine and R), and easy to apply to other surveys.

The Theory of Island Biogeography

Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Google Earth Engine for geo-big data applications: A meta-analysis and systematic review

Abstract: Google Earth Engine (GEE) is a cloud-based geospatial processing platform for large-scale environmental monitoring and analysis. The free-to-use GEE platform provides access to (1) petabytes of publicly available remote sensing imagery and other ready-to-use products with an explorer web app; (2) high-speed parallel processing and machine learning algorithms using Google’s computational infrastructure; and (3) a library of Application Programming Interfaces (APIs) with development environments that support popular coding languages, such as JavaScript and Python. Together these core features enable users to discover, analyze and visualize geospatial big data in powerful ways without needing access to supercomputers or specialized coding expertise. The development of GEE has created much enthusiasm and engagement in the remote sensing and geospatial data science fields. Yet after a decade since GEE was launched, its impact on remote sensing and geospatial science has not been carefully explored. Thus, a systematic review of GEE that can provide readers with the “big picture” of the current status and general trends in GEE is needed. To this end, the decision was taken to perform a meta-analysis investigation of recent peer-reviewed GEE articles focusing on several features, including data, sensor type, study area, spatial resolution, application, strategy, and analytical methods. A total of 349 peer-reviewed articles published in 146 different journals between 2010 and October 2019 were reviewed. Publications and geographical distribution trends showed a broad spectrum of applications in environmental analyses at both regional and global scales. Remote sensing datasets were used in 90% of studies while 10% of the articles utilized ready-to-use products for analyses. Optical satellite imagery with medium spatial resolution, particularly Landsat data with an archive exceeding 40 years, has been used extensively. Linear regression and random forest were the most frequently used algorithms for satellite imagery processing. Among ready-to-use products, the normalized difference vegetation index (NDVI) was used in 27% of studies for vegetation, crop, land cover mapping and drought monitoring. The results of this study confirm that GEE has and continues to make substantive progress on global challenges involving process of geo-big data.

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Abstract: Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.