Camille H. Ross, Ph.D.

Research

Estimating ecological fields from sparse, irregular, effort-limited observations — and keeping the observation process separate from the ecological one.

Our knowledge of whales at sea is contingent upon our ability to find them: whether visually from an airplane or vessel or acoustically from hydrophones. Both of those observation types are shaped as much by the survey as by the animal’s behavior and environmental conditions.

My work as a biological oceanographer focused on conservation considers what we can infer from the data we are able to collect. I build models that separate the observation process from the ecological process, so that what comes out the other end is an estimate of what is actually there rather than an estimate of where we happened to look. That matters because these models are not academic exercises: they are considered by the entities who build the tools used to manage the critically endangered North Atlantic right whale.

Whale prey

Right whales eat copepods — primarily Calanus finmarchicus. However, a whale does not necessarily respond to average copepod abundance, but rather to whether a prey patch is dense enough that feeding on it is energetically advantageous. That distinction matters especially for modeling.

Part of my work involves using environmental conditions to explain and predict prey aggregations that exceed a whale-specific feeding threshold. We refer to these high-density prey aggregations as τ-patches. This modeling strategy differs from abundance-based models: we are able to interpolate zooplankton data in a given region, like the Gulf of Maine, from the perspective of a foraging whale. This led to new prey covariate fields that have proven useful in right whale density surface models. The approach has been extended beyond C. finmarchicus to Centropages typicus and Pseudocalanus spp., revealing that the τ-patch probability shifts not only between species, but across space and time.

Key papers: Estimating right whale prey based on Calanus finmarchicus thresholds · Beyond Calanus: changes to the copepod community · Calanus species and foraging habitat in Canadian waters

Code: taupatch implements the threshold patch models; datamatch and derivoce build the environmental covariates.

Twelve monthly maps of the northeast US shelf and Gulf of Maine, shaded by predicted probability of a high-density Calanus finmarchicus patch. Probability is highest across the Gulf of Maine from May through September and lowest in late autumn and winter.
Predicted Calanus finmarchicus τ-patches by month, at a threshold of 10,000 individuals m−2. From Ross et al. (2023), Marine Ecology Progress Series 703:1–16. doi.org/10.3354/meps14204. CC BY 4.0.

Whale habitat preference

Density surface models turn systematic line-transect survey data into maps of animal density. They are only as good as their treatment of the observation process: how detectability falls off with distance, how effort is distributed, how much of the variance in a map is survey design rather than biology.

A central theme of my work has been designing prey covariates and testing how they can improve endangered species habitat models. Marine habitat models conventionally rely upon chlorophyll-a concentration or NPP models as a proxy for zooplankton prey. However, satellite- or model-derived chlorophyll products are several steps removed from the food right whales are actually targeting. We have found that substituting whale-specific prey fields for prey proxies improves right whale density model distributions: in a recent paper, combining three right whale-specific copepod prey fields improved density estimates to closer represent what we would expect in the field.

This work emphasizes the need for continued surveying and sampling of both whales and zooplankton.

Key papers: Incorporating prey fields into right whale density surface models · Impacts of an oceanographic regime shift on U.S. right whale density estimates (in review)

Code: narwcr and distsamp handle survey ingest and segmentation; dsfit fits and compares detection functions on effective strip half-width rather than the Akaike information criterion (AIC) alone.

Twelve monthly anomaly maps from Cape Hatteras to the Scotian Shelf, showing the change in predicted right whale density when copepod prey fields replace conventional covariates. Red indicates higher predicted density, blue lower; the strongest increases are in April and May.
Change in predicted right whale density when all three copepod prey fields are included, against the baseline model. These are anomalies, not absolute density. From Ross et al. (2025), Endangered Species Research 58:67–84. doi.org/10.3354/esr01435. CC BY 4.0.

Looking to the future

The Gulf of Maine is warming faster than the rest of the ocean, and right whales have already redistributed in ways that broke the assumptions behind existing management strategies and surveying designs. Projecting habitat suitability forward into 2050 under different IPCC climate scenarios suggests the trend continues: declining suitability across much of the Gulf of Maine from mid-summer into autumn, with a north-eastward shift toward the Scotian Shelf, the Bay of Fundy, and the Newfoundland and Labrador shelves — much of it outside where conservation effort has been historically concentrated.

Change of this scale presents on decadal as well as on interannual and monthly scales. The Gulf of Maine has a documented history of unprecedented oceanographic conditions, arriving with increasing frequency than the decadal-scale trends alone would predict. That raises a harder question than “what will the mean look like in 2050”: what happens to a density estimate built across distinct oceanographic regimes, when the relationship the model learned no longer holds? This is the underlying question of much of my current work.

Key papers: Projecting right whale habitat suitability for 2050 · Foraging habitat under future climate scenarios · The surprising oceanography of the Gulf of Maine

Twenty-four small maps of the Gulf of Maine showing modelled right whale habitat suitability in the year 2050, arranged in four labelled blocks for July, August, September and October, each under two emissions scenarios and three chlorophyll assumptions. Suitability concentrates along the shelf edge and the eastern Gulf.
Year-2050 habitat suitability projections by month, under two emissions scenarios (RCP 4.5 and 8.5) and three chlorophyll assumptions: HC, half present-day chlorophyll; SC, the same as present day; DC, double. Colour is the modelled likelihood of suitable habitat, from 0 to 1. From Ross et al. (2021), Elementa: Science of the Anthropocene 9(1):00058. doi.org/10.1525/elementa.2020.20.00058. CC BY 4.0.

Methods and tools

  • Observation process — detectability modeling from line-transect data, density surface models with explicit treatment of survey effort, propagating observation uncertainty into management-facing estimates
  • Statistical learning — generalized additive models, boosted regression trees, random forests, artificial neural networks (including CNNs trained from scratch)
  • Time series — spectral analysis of multi-decadal oceanographic and biological series: Fourier transforms and periodograms, coherence, filtering, autocorrelation structure
  • Data sources — Copernicus Earth Observation and NASA EarthData products, ocean physics model output (HYCOM, GLORYS, FVCOM, DFO internal models), aerial and vessel survey archives, passive acoustic monitoring
  • Practice — R, Python, git, bash, Linux; reproducible pipelines; R Shiny and Leaflet

Background reading

Foundational works for this research, including modeling methods and the current state of the ecosystem.