Locating and prioritizing areas with high conservation value in the Saint John River watershed
Bibliographic record
Abstract
Information on the distribution of species-at-risk habitat facilitates conservation efforts of those species, and enables the development of a more accurate landscape-scale conservation plan.Geographical information system (GIS)-based predictive habitat mapping can greatly improve this process by reducing the required amount of time-and resource-consuming field surveys.The purpose of this study was to explore the possibilities of providing a semi-automated GIS-based approach to predictive at-risk species habitat distribution modeling.The study area was the New Brunswick portion of the upper and middle Saint John River watershed in western New Brunswick, Canada.First, the most important habitat factors were identified for 175 terrestrial species-at-risk by comparing point observation data to selected habitat characteristic features.The results were then used to locate areas with similar habitat characteristics, and -thereby -potential habitat of the species.These steps were performed using ArcGIS software, where a series of models were built to automate the process, in order to facilitate the processing of large amounts of data.Four species from different species groups were selected to illustrate the developed method: Bi cknell's thrush (Catharus bicknelli) from birds, the spine-crowned clubtail (Gomphus abbreviatus) from insects, the wood turtle (Glyptemys insculpta) from reptiles, and the little bluestem (Schizachyrium scoparium) from plants.The results of the study indicate a correspondence between model-generated habitat characteristics and those defined in literature.A series of habitat characteristics match those expressed in literature for the selected species, but some key habitat characteristics, most notably water vicinity, were not allocated a sufficient preference value.The results highlight the need for precise species observation point data, as well as a set of habitat factors that accurately describe the habitat quality for each individual species.The resulting potential habitat distribution maps of individual species illustrate areas with varying degrees of habitat quality.This data on either individual species or species groups can be used for a variety of planning or research projects.Based on the results of the analyses performed in this thesis, the feasibility of spatially optimizing the most important habitat areas for conservation was assessed.The habitat distribution data created with this method can be used to produce a strategic conservation plan, identifying priority locations for conservation and providing an insight into the feasibility of their proposed conservation.A number of software can be used to carry out the spatial optimization.This would support important conservation efforts in the upper and middle Saint John River watershed area.However, since high value potential habitat does not as such indicate species presence or abundance, any management decisions based on the results of these analyses should be supported by on-site surveys.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".