National assessment on the status, trends and impacts of marine non-indigenous species for the European Union marine strategy framework directive
Bibliographic record
Abstract
The Marine Strategy Framework Directive (MSFD) of the European Union aims to enhance the protection of marine ecosystems by assessing Good Environmental Status using several descriptors. Descriptor 2 of the MSFD states that the status of marine non-indigenous species (NIS) should be assessed using specified criteria regarding newly introduced NIS, abundance, and spatial distribution of established NIS, as well as proportions of native species groups adversely altered due to NIS. The current study presents a national NIS indicator assessment for the six sub-basins associated with the Finnish marine waters. The study additionally aimed to develop a methodology to assess changes in native species communities over time. A new biodiversity index was created for this purpose by combining the Gini-Simpson index, and species and phyla richness into a Community Biodiversity Index (CBI). CBI was calculated to measure changes in biodiversity within the soft-bottom benthic communities of the study area, and correlation analyses were conducted between the index scores and NIS proportion. Overall, 34 NIS have been recorded from the Finnish marine waters by 2022, and more than half of them have arrived since 1992. Spatial distribution of three established non-indigenous taxa (Marenzelleria spp., Cercopagis pengoi and Neogobius melanostomus) has expanded during the latest assessment periods and nowadays cover most of the national marine waters. Meanwhile, abundance of Marenzelleria spp. in benthic communities has increased heavily in most sub-basins. The study outcomes indicated that NIS proportion correlated significantly with the CBI within the soft-bottom benthic communities. This correlation was significantly negative for the samples with a higher NIS proportion than the median, suggesting that when NIS became more dominant, biodiversity of the communities decreased. The current study provides an applicable method to evaluate changes in biodiversity for various species groups, and how to link them to changes in the distribution and abundance of established NIS. It can be concluded that the national biological monitoring program has severe temporal and spatial gaps, as the national monitoring data enabled the assessment on native species groups, adversely altered due to NIS to be completed only for one species group, soft-bottom macrozoobenthos. Most importantly, the study emphasised the crucial need for management actions to prevent further NIS introductions.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".