Invasion Biology Modeling
My PhD research focuses on the development of forecasting, risk, and impact assessment models of non-native species. By combining computational simulations with both biological and sociological data, this research aims to provide decision support to resource managers and policy makers.
With an emphasis on uncertainty quantification through the construction of Bayesian models, I analyze the implications of various human and biological factors on the spatial spread of fresh water invasive species, including: 1) environmental and demographic stochasticity, 2) dispersal network structure, and 3) human behavioural feedbacks to policy decisions. Together, this research provides novel insights into both ecological processes and environmental policy.
Incorporating multiple levels of stochasticity and epistemic uncertainty using hierarchical Bayesian models to forcast invasions.
General state-space modeling of spatio-temporal processes.
Corey Chivers (2013). rvmapp: Validation Metric Applied to Probabilistic Predictions. R package version 0.1-1.Pre-released.
Corey Chivers (2012). MHapaptive: General Markov Chain Monte Carlo for Bayesian Inference using adaptive Metropolis-Hastings sampling. R package version 1.1-8.
NCEAS The National Center for Ecological Analysis and Synthesis (Non-Native Forest Pests and Pathogens Working Group)
CAISN Canadian Aquatic Invasive Species Network
Chivers, C., & Leung, B. (2014) Modelling responses to management intervention for controlling the spread of freshwater invasives. in prep.
Chivers, C., & Leung, B. (2014) Estimating the probability of establishment and spread of biological organisms: Issues of uncertainty, detection and presence-only data. in prep.
Chivers, C., Leung, B., & Yan (2013) Probabilistic predictions in ecology and their validation. in review.
Low-Decarie, E., Chivers, C. & Granados, M. (2013) Rising complexity and falling predictive power in ecology. in review.
Bradie, J., Chivers, C. & Leung, B. (2013) Importing risk: quantifying the propagule-pressure establishment relationship at the pathway level. Diversity and Distributions. doi:10.1111/ddi.12081
Chivers, C & Leung, B. (2012) Predicting invasions: Alternative models of human-mediated dispersal and interactions between dispersal network structure and Allee effects. Journal of Applied Ecology, 49: 1113-1123. doi:10.1111/j.1365-2664.2012.02183.x
Aukema JE, Leung B, Kovacs K, Chivers C, Britton KO, et al. (2011) Economic Impacts of Non-Native Forest Insects in the Continental United States. PLoS One 6(9): e24587. doi:10.1371/journal.pone.0024587
Chivers, C. (2013) From Whale Calls to Dark Matter: Competitive Data Science with R and Python. Montreal Python. (Montreal, QC. June, 2013)
Chivers, C. & Leung, B. (2013) Implications of uncertainty: Bayesian modelling of aquatic invasive species spread. International Conference on Aquatic Invasive Species. (Niagara Falls, ON. April, 2013)
Chivers, C. (2013) Future Avenues for Open Data. Open Data Exchange. (Montreal, QC. April, 2013)
Chivers, C. (2013) Predictive Ecology and Management Decisions Under Uncertainty. McGill BGSA Organismal Seminar Award. (Montreal, QC. January, 2013)
Chivers, C. & Leung, B. (2011) Interactions between dispersal network structure and Allee Effects. Quebec Centre For Biodiversity Science. (Montreal, QC. September, 2011) (french)
Chivers, C., Guillemette, J., Ragan, K. (2012) Predictive Modelling of Student Grades and Comparisons with Conceptual Gains. The Society for Teaching and Learning in Higher Education. (Montreal, QC. June, 2012)
Chivers, C. (2009) Why Aren't We All Bayesians? Paris Interdisciplinary PhD Symposium. (Paris, France. December, 2009)
T-PULSE Teaching Fellowship
BIOL645 Biodiversity Field Course (Lecturer & Section Designer)
BIOL202 Introduction to Genetic Analysis (TA)
BIOL373 Biometry (Guest Lecturer & TA)
BIOL200 Molecular and Cell Biology (TA)
Skillsets Learning to Teach
BGSA R/Statistics Workshops
Editorial and Review Service
Highly proficient in Linux OS and associated data manipulation tools (sed, grep, awk)
Expert level R programming for predictive modelling and data visualization
Skilled in both low level programming (c++) and scripting languages (Bash, python)
Proficient with open source Geographic Infomation Systems (GIS) QGIS and GRASS.
Experience with SQL and No-SQL (couchdb) databases
Experience using scalable High Performance Computing (HPC) resources including CLUMEQ and AWS
Experience using Big Data technologies including MapReduce using Appache Hadoop
Experience with version control (Git)