Towards harmonized standards for freshwater biodiversity monitoring and biological assessment using benthic macroinvertebrates
Bibliographic record
Abstract
Monitoring programs at sub-national and national scales lack coordination, harmonization, and systematic review and analysis at continental and global scales, and thus fail to adequately assess and evaluate drivers of biodiversity and ecosystem degradation and loss at large spatial scales. Here we review the state of the art, gaps and challenges in the freshwater assessment programs for both the biological condition (bioassessment) and biodiversity monitoring of freshwater ecosystems using the benthic macroinvertebrate community. To assess the existence of nationally- and regionally- (sub-nationally-) accepted freshwater benthic macroinvertebrate protocols that are put in practice/used in each country, we conducted a survey from November 2022 to May 2023. Responses from 110 respondents based in 67 countries were received. Although the responses varied in their consistency, the responses clearly demonstrated a lack of biodiversity monitoring being done at both national and sub-national levels for lakes, rivers and artificial waterbodies. Programs for bioassessment were more widespread, and in some cases even harmonized among several countries. We identified 20 gaps and challenges, which we classed into five major categories, these being (a) field sampling, (b) sample processing and identification, (c) metrics and indices, (d) assessment, and (e) other gaps and challenges. Above all, we identify the lack of harmonization as one of the most important gaps, hindering efficient collaboration and communication. We identify the IUCN SSC Global Freshwater Macroinvertebrate Sampling Protocols Task Force (GLOSAM) as a means to address the lack of globally-harmonized biodiversity monitoring and biological assessment protocols.
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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 | 0.255 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".