Research & Initiatives

The SEaDAL lab's primary research objective is to use statistical modelling to uncover the underlying biological &/or ecological mechanisms generating these datasets.

The Statistical Ecology at Dal (SEaDAL) Laboratory brings together researchers from various disciplines at Dalhousie University (DAL), to develop statistical methods for ecology.

Joanna Mills Flemming

Population structure, estimating effective population size; population dynamics models; abundance estimation using genomics & the Close Kin Mark Recapture (CKMR) approach; advancing spatio-temporal stock assessment models to integrate high-resolution seafloor mapping data for more sustainable, ecosystem-based, harvest strategies.

Chris Field

Robustness in tracking & fisheries models.

Orla Murphy

Extreme value analysis with applications to the environment, e.g., coastal, & in-land flood risk analyses.

Andrew Irwin

Anticipating consequences of climate change in the marine ecosystem; environmental niche models in plankton ecology; detecting & modelling resource allocation in marine microbes.

Theo Michelot

Hidden Markov models for ecology; inference for stochastic processes; habitat selection modelling.

Michael Dowd

Nearshore temperature monitoring & modelling for seagrass ecosystems; development of a real-time forecasting system for Southern Resident Killer Whales; machine learning for acoustic whale detection and species classification.

Increasingly large amounts of ecological data are being collected each year, due to technological developments such as satellite imagery, telemetry tags, and acoustic sensors. These data contain detailed information about ecological processes that have traditionally been difficult to observe & study. Our primary research objective is to use statistical modelling to uncover the underlying biological &/or ecological mechanisms generating these datasets.

We are a highly collaborative and interdisciplinary team that develops flexible &interpretable approaches. We believe that statisticians have a critical role to play at every stage of analysis, & we are interested in helping scientists and policymakers formulate research questions, design data collection, assess modelling assumptions, & make decisions in the face of uncertainty. We also believe that providing user-friendly software is key to making our work accessible, & we maintain several R packages for the analysis of spatial & time series data.

DAL is a global leader in marine science, and much (but not all!) of our research is turned towards the sea. In particular, we have close links to the Ocean Tracking Network (OTN), the Ocean Frontier Institute (OFI), & the Seascape Ecology and Mapping (SEAM) Lab all headquartered here at DAL. We also have a growing number of national (e.g., Fisheries and Oceans Canada (DFO)) &international partners (National Institute of Aquatic Resources (DTU Aqua), National Oceanic and Atmospheric Administration (NOAA), Institute for Clinical Evaluative Sciences (ICES), etc.).

Meet the Team

Read Publications

See Collaborators