An experimental test of the habitat‐amount hypothesis for saproxylic beetles in a forested region
Bibliographic record
Abstract
Abstract The habitat‐amount hypothesis challenges traditional concepts that explain species richness within habitats, such as the habitat‐patch hypothesis, where species number is a function of patch size and patch isolation. It posits that effects of patch size and patch isolation are driven by effects of sample area, and thus that the number of species at a site is basically a function of the total habitat amount surrounding this site. We tested the habitat‐amount hypothesis for saproxylic beetles and their habitat of dead wood by using an experiment comprising 190 plots with manipulated patch sizes situated in a forested region with a high variation in habitat amount (i.e., density of dead trees in the surrounding landscape). Although dead wood is a spatio‐temporally dynamic habitat, saproxylic insects have life cycles shorter than the time needed for habitat turnover and they closely track their resource. Patch size was manipulated by adding various amounts of downed dead wood to the plots (~800 m³ in total); dead trees in the surrounding landscape (~240 km 2 ) were identified using airborne laser scanning (light detection and ranging). Over 3 yr, 477 saproxylic species (101,416 individuals) were recorded. Considering 20–1,000 m radii around the patches, local landscapes were identified as having a radius of 40–120 m. Both patch size and habitat amount in the local landscapes independently affected species numbers without a significant interaction effect, hence refuting the island effect. Species accumulation curves relative to cumulative patch size were not consistent with either the habitat‐patch hypothesis or the habitat‐amount hypothesis: several small dead‐wood patches held more species than a single large patch with an amount of dead wood equal to the sum of that of the small patches. Our results indicate that conservation of saproxylic beetles in forested regions should primarily focus on increasing the overall amount of dead wood without considering its spatial arrangement. This means dead wood should be added wherever possible including in local landscapes with low or high dead‐wood amounts. For species that have disappeared from most forests owing to anthropogenic habitat degradation, this should, however, be complemented by specific conservation measures pursued within their extant distributional ranges.
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 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 | 0.001 | 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".