Measuring the Spatial Structure of Seaweed Habitats in a Changing Environment: Implication for the Food Web
Bibliographic record
Abstract
Kelp beds around the globe are shifting towards shorter, bushier seaweeds which have a radically different spatial structure. This is particularly true for the Gulf of Maine where invasive seaweeds have come to dominate habitats to change the visual landscape. In parallel, populations of inhabitant species (meso-invertebrates), those that are at the base of the food web, are exploding. Most metrics for habitat architecture are based on two dimensional measurements, but habitats are three dimensional. To understand how invasive seaweeds change the three-dimensional biological structure of seaweeds, we developed a new method (Spherical Space Analysis) for characterizing their spatial structure using sampled specimens. This method characterizes the three dimensional volume distribution by size of interstitial spaces for three of seaweeds. This method was then used to predict the ecological function of the novel seaweed habitat using abundances and size ranges of meso-invertebrates from sampled seaweeds. Spherical space analysis provides a mechanism for understanding how the spatial architecture of a seaweed environment mediates the network of feeding interactions occurring within it. This has implications for food webs and restoration efforts. Presenter Bio Colin Ware Colin Ware is a member of the Center for Coastal and Ocean Mapping and Director of the Data Visualization Research Lab. Dr. Ware's position is split between the Ocean Engineering and Computer Science Departments. Dr. Ware has a background in human/computer interaction (HCI) and has been instrumental in developing a number of innovative approaches to the interactive 3-D visualization of large data sets. As a founding member of the University of New Brunswick Ocean Mapping Group, Dr. Ware designed many of the algorithms and interactive techniques that were that were incorporated into Fledermaus, a 3D visualization package and into CARIS HIPS, the most commonly used commercial hydrographic processing package. Jenn Dijkstra Dr. Jenn Dijkstra is a Research Assistant Professor in The School of Marine Science and Ocean Engineering and the Center for Coastal and Ocean Mapping. She serves on the New Hampshire commission for Coastal Marine Natural Resources and Environment. Her research interests include patterns and processes of biodiversity and biogeography, habitat structure, and introduced species. In these areas, her research focuses on 1) Biogeography of marine species, 2) Introduced species, 3) Biogenic structure and ecosystem function and 4) Integration of data collected by in-situ sampling and remote-sensing techniques to identify and characterize marine species assemblages. Dr. Dijkstra received a B.A. from the University of New Brunswick (Canada), a M.Sc. in Marine Biology from the University of Bremen (Germany) and a Ph.D. from the University of New Hampshire.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".