The precipitation levels during the warmest quarter are the primary factor influencing the spatial distribution of Opatrum sabulosum
Bibliographic record
Abstract
The present investigation aims to analyse the influence of bioclimatic predictors on the geographical distribution of the species Opatrum sabulosum (Linnaeus, 1761) and to predict changes in its range in the context of global warming. Opatrum sabulosum, a species belonging to the Tenebrionidae family, exhibits a high degree of plasticity in its environmental requirements, yet remains susceptible to the impacts of climate change. The maximum entropy algorithm (MaxEnt) was employed to model the ecological niche, with the species distribution data from the GBIF database and key bioclimatic variables such as temperature, precipitation, and their seasonality being utilised. Forecasts were made for the present and up to 2080 under four climate change scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. The results indicate that the key factors affecting the distribution of O. sabulosum are the minimum temperature of the coldest month, the average temperature of the coldest quarter, and the amount of precipitation in the warmest and wettest quarters. The analysis indicates that, under current conditions, the most favourable habitats for the species are located in Western Europe, southern Britain, Scandinavia and the northern Black Sea region. In the context of a moderate warming scenario (SSP1-2.6), the species' range is projected to expand in an eastward and northeasterly direction, driven by rising average temperatures and an enhanced water balance. Conversely, under extreme scenarios (SSP3-7.0, SSP5-8.5), a decline in habitat quality in the southeastern part of the species' range is projected due to elevated temperatures, reduced humidity, and instability in climatic parameters. The practical significance of these results lies in the possibility of using these data to develop adaptation strategies for biodiversity conservation and effective management of natural resources. Forecasts of the distribution of O. sabulosum can serve as a basis for assessing the risks of ecosystem changes and creating new protected areas. Information regarding the species' sensitivity to climate change is also important for the sustainable development of agroecosystems, in which this species plays a role in maintaining soil fertility. The findings of this study are directly pertinent to the attainment of the Sustainable Development Goals (SDGs) established by the United Nations in 2015. Specifically, the study contributes to the implementation of SDG 13 'Combat climate change' by providing a more nuanced understanding of the effects of climate change and the adaptation of ecosystems to new conditions. The findings are also pertinent to SDG 15, 'Conserve terrestrial ecosystems', as predicting species distribution helps to conserve biodiversity and restore degraded ecosystems. The integration of these findings into environmental management practices is expected to contribute to ensuring environmental sustainability, efficient use of natural resources, and the creation of a harmonious environment for future generations. Prospects for further research include long-term monitoring of O. sabulosum populations, integration of genetic data to assess the species' adaptive potential, and expanding the analysis to include anthropogenic factors such as land use change, urbanisation and agricultural activities. This will allow more accurate forecasting of ecosystem changes in the future.
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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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".