Environmental Statistics seminars

Satellite sea-ice concentration retrievals are commonly assimilated using a constant observational uncertainty of 0.10–0.15. This research instead estimates uncertainty within the assimilation system through an iterated Desroziers diagnostic constrained to the physical 0–1 concentration range. Two passive-microwave products and one optical product in both hemispheres show strongly state-dependent errors: roughly 0.02–0.10 near open water or complete cover, and 0.20–0.35 around half cover. Complete cover during melt remains difficult to constrain because the model rarely reproduces it. Comparisons among three collocated satellite products and published retrieval uncertainties support the pattern. The results suggest constant uncertainty overweights intermediate retrievals and underweights values near zero and one, with thin and ponded ice contributing errors beyond instrument noise.

This project tests whether nearby National Weather Service METAR observations can fill missing pressure and temperature records at California's Sky Oaks Field Station, where measurements support carbon-cycle research. Linear mixed-effects models and inverse-distance interpolation combine archived observations from the Iowa Environmental Mesonet with USGS elevation data. The pressure model is statistically significant but its 1.21-millibar mean absolute error exceeds the one-millibar target. The temperature model finds no significant correlation and has a 2.60°F mean absolute error. An eddy-covariance tower was also restored to collect meteorological and gas-flux data. The work was completed through a NOAA NERTO internship mentored by Ivory Small and demonstrates both the potential and limitations of reconstructing local environmental observations.

NOAA's Mussel Watch records are examined as a unified framework for coastal contamination. The study combines sediment chemistry, toxicity, benthic condition and tissue measurements, using AMBI and M-AMBI scores at 732 national assessment sites and 33 Hudson–Raritan stations. Random-forest and Bayesian models associate urbanization with benthic condition in the New York region (ρ=0.62), but not nationally (ρ=0.05). A Delft3D estuary model reproduces observed tides and predicts increased deep-channel bed shear under sea-level rise. The work asks whether sediment contaminants mediate urbanization impacts and whether legacy cleanup remains effective as sea level changes. It was conducted through a NOAA NERTO graduate internship with Kimani Kimbrough and Erik Davenport at NCCOS.

How do elementary biological events give rise to ecological community patterns, epidemic behaviour and collective population dynamics? This seminar develops a mechanistic view using Markov jump processes for birth, death, competition, dispersal, infection and recovery. These models connect individual events to biodiversity gradients, species abundance distributions and disease persistence. It examines the roles of demographic stochasticity, environmental variation, spatial heterogeneity and species interactions, highlighting mathematical similarities and differences across ecological and epidemiological systems. The inverse problem is also considered: inferring underlying mechanisms and interaction structures from macroscopic fluctuations and time-series data. Together, stochastic modelling and statistical inference provide a framework for studying ecological complexity and community stability.

Beth Gardner introduces spatial capture-recapture models for estimating wildlife population dynamics and movement. The seminar considers observations from camera traps and genetic identification using hair or scat, showing how the modeling framework can accommodate different sampling methods, time scales and movement processes. Gardner develops the Bayesian formulation through worked examples in R with NIMBLE, then discusses extensions and audience questions. The methods support estimates of demographic rates, spatial distributions and habitat relationships for conservation and management. The organizer explicitly schedules this occurrence at 15:00 Eastern time, rather than the series’ usual time.

Sanne Evers introduces the first forecasting challenge organized by the European Ecological Forecasting Initiative. The challenge uses long-running observations from Doñana National Park in southern Spain to support short-term predictions of vegetation indices and the abundance of multiple shrub species. Forecasting these changes can inform habitat management, productivity assessments and conservation decisions under climatic and hydrological pressures. The seminar explains why Doñana’s range of habitats and monitoring infrastructure offer a useful setting for comparing predictions before new observations become available. It also discusses lessons from developing the challenge and planned next steps, with participation encouraged across ecological disciplines and levels of forecasting experience.

This panel examines how biodiversity science can inform decisions in business, finance and government. It assesses what is known about biodiversity status and trends in marine and terrestrial systems, which dimensions matter for decisions, and how to navigate financial-disclosure frameworks, science-based targets, national indicators and biodiversity credits. The discussion considers emerging methods including genomics, AI-based monitoring, functional traits, multiple knowledge systems and global indices. It asks how these approaches can support ecological recovery without reinforcing extractive measurement or governance, and how metrics can respect Indigenous rights, knowledge and decision-making. The panel also recognises that better measurement is only one component of changing business impacts on nature.

This lecture examines how temperature change is measured across the last two centuries. It compares instruments on the ground, satellite observations and indirect evidence for earlier periods. The discussion addresses the construction of global averages from uneven observations, possible urban influences and changes in temperature extremes.

Poor air quality in cities is a significant threat to health and life expectancy, with over 80% of people living in urban areas exposed to air quality levels that exceed World Health Organisation limits. In this session, I present a multi-resolution multi-task framework that handles evidence integration under varying spatio-temporal sampling resolution and noise levels. We have developed both shallow Gaussian Process (GP) mixture models and deep GP constructions that naturally handle this evidence integration, as well as biases in the mean. These models underpin our work at the Alan Turing Institute towards providing spatio-temporal forecasts of air pollution across London. We demonstrate the effectiveness of our framework on both synthetic examples and applications on London air quality. For further information go to: https://www.turing.ac.uk/research/research-projects/london-air-quality. Collaborators: Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang and Mark Girolami.