Ecosystem services provided by spiders
Bibliographic record
Abstract
Spiders, ubiquitous and abundant predators in terrestrial ecosystems, often are the subjects of an unjust negative perception. However, these remarkable creatures stand as unsung heroes within our ecosystems, contributing a multitude of ecosystem services critical to human well-being. Here, we describe the diverse spectrum of ecosystem services offered by spiders and their potential to inspire or directly provide nature-based solutions. Provisioning services include the versatile uses of silk-like and other materials, inspiration for biomimetic technology, medicines derived from venom, hemolymph and silk, bio-insecticides that offer eco-friendly alternatives to synthetic chemicals, food sources for various human communities worldwide, and unconventional yet increasingly valued pets. Regulating services provided by spiders extend to vital roles in pest suppression across diverse agricultural settings, mitigating diseases by curbing insect-mediated pathogen dispersal, and controlling invasive species. Supporting services offered by spiders are equally extensive, involving nutrient cycling through the breakdown of organic matter, acting as food sources for predators, or creating habitats for other organisms. Beyond their tangible contributions, spiders hold a significant cultural and spiritual heritage globally and are integral to many traditional medicine practices. They inspire contemporary culture, provide educational value, contribute to mental health improvement, evoke a sense of place, offer models for scientific discovery, and are commonly employed for monitoring biodiversity and ecosystem health. To pave the way for future research, we present suggestions for exploring and quantifying the economic value of ecosystem services by spiders. While many of these services are well established and studied from various perspectives, others harbour untapped potential. Leveraging what nature inherently provides, these nature-based solutions offer avenues to address challenges such as biodiversity erosion and societal needs. By restoring, preserving, or mimicking natural processes of spiders, we can enhance or provide essential ecosystem services, harnessing the full potential of spiders and the web of benefits they bring us.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".