Using habitat suitability models to predict changes in seagrass distribution caused by water management practices<sup>1</sup>This article is derived from a special session entitled “A New Hydrology: Inflow Effects on Ecosystem Form and Functioning” that took place at the February 2011 ASLO Aquatic Sciences conference in San Juan, Puerto Rico.
Bibliographic record
Abstract
Habitat suitability (HS) models can help elucidate the relationship between organisms and their environment and simulate the potential impacts of human activities on species distributions. Here, we developed HS models for Thalassia testudinum and Halodule wrightii , two seagrasses found in Biscayne Bay (Florida, USA). These species are mostly found in nearshore habitats of the bay that are highly susceptible to changes in water quality because of their proximity to the city of Miami and activities and projects associated with the restoration of the Florida Everglades . The HS models parameterized with data collected at >900 sites highlighted salinity as a key factor determining habitat suitability for these seagrass species. Thalassia ’s suitable habitat was associated with higher, more stable salinity compared with that of Halodule , which was associated with lower salinity and shallower depths. Both species benefited under a simulated scenario of increased freshwater flow, but Halodule’s suitable habitat increased by 71% compared with that of Thalassia’s, which exhibited only an 18% increase. HS models such as those described here provide spatial modeling tools that can contribute science-based input into the management of coastal resources within an adaptive management framework.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".