Conservation Biology in Poorly Studied Freshwater Ecosystems: From Accelerated Identification of Water Quality Bioindicators to Conservation Planning
Bibliographic record
Abstract
The Tigris and Euphrates Rivers and their tributaries form the arteries of life in the central part of the Middle East, where climate change and anthropogenic disturbance have been evident in recent decades.While the Tigris River has a long history of human use, the conservation status for the majority of its basin is poorly known.In addition, planning for conservation, given limited time, funds, and prior information, has remained a challenge.In my dissertation research, I sampled 53 randomly selected sites in the Kurdistan Region (the KR) of northern Iraq, a poorly studied region of the Upper Tigris and Euphrates freshwater ecoregion, for water quality bioindicators, mayflies (Insecta, Ephemeroptera), stoneflies (Plecoptera), and caddisflies (Trichoptera) (a.k.a.EPT).I identified the mayflies to the finest possible taxonomic level and created the first Iraqi checklist and larval key to nine families, nine subfamilies, 19 genera, and 13 subgenera, and supported it with 117 state-of-the-art scientific illustrations using fresh specimens collected during my study (Chapter 1).I performed an initial species morphological identification for mayflies and stoneflies, then identified them genetically after sequencing the full-length of the mitochondrial cytochrome oxidase subunit 1 (COI) gene (658 base pairs).I introduced Genetic Similarity Blocks (GSBs), a genetic-based analysis which was used along with morphology and other genetic-based analyses to overcome the taxonomic impediment and accelerate species identification.I delineated Operational Taxonomic Units (OTUs) using genetic-based analyses, then matched OTUs to delineate Species-Like Units (SpLUs).I compared and contrasted SpLUs morphologically and found five stonefly and more than 55 mayfly taxa, the majority of them being new records for Iraq, and many of them potentially new to science (Chapter 2).I identified 76 planning units within aquatic ecosystems in the KR and prioritized a subset of them for EPT conservation using my mayfly and stonefly data as well as caddisfly data from other studies.I identified samples of healthy aquatic habitats from this subset and used them along with various predictor variables to predict the distribution of healthy aquatic habitats across the entire KR.I identified one natural lake and 23 stream segments as habitats of conservation priority in the KR (Chapter 3).In my dissertation, I showed that in developing countries where knowledge about aquatic ecosystems and most extant species is unavailable, conservation priorities can still be identified after rapid assessment for water quality bioindicators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".