MINI‐REVIEW: Habitat analogues for reconciliation ecology in urban and industrial environments
Bibliographic record
Abstract
Summary 1. Current views of anthropogenic environments emphasize the extreme novelty of urban and industrial ecosystems. Proponents of reconciliation ecology argue that we need to use such habitats to conserve biodiversity, given the inadequacy of natural reserve systems. 2. Some of the harshest anthropogenic ecosystems may be able to support indigenous biodiversity due to their structural or functional resemblance to natural ecosystems, habitats, or microsites that may be present in the region but not part of the historic ecosystem on a particular site. Here we review recent work that evaluates similarities between urban and industrial ecosystems and natural analogues, and explore the potential for these in reconciliation ecology. 3. We find that artificial habitats represent a gradient of ecological novelty which may be independent of the degree of human influence. While hard‐surfaced habitats such as walls and quarries are the most investigated artificial analogues (of natural rock pavements and cliffs), there are many other examples spanning a range of habitats in both terrestrial and marine settings. Analogous ecosystems may be present in the region but limits to dispersal can prevent appropriate species from reaching urban or industrial sites, and small differences in abiotic conditions can sometimes prevent colonization by native biota in otherwise similar artificial habitats. We suggest that a search for habitat analogues represents an important principle to guide reconciliation ecology in urban and industrial lands. In constrast, analogous ecosystems may also support pest species that exploit the similarities between anthropogenic habitats and their ancestral habitats. 4. Synthesis and applications. Identifying analogous habitats and ecosystems could enhance biodiversity conservation and ecosystem services in anthropogenic environments. Abiotic and biotic differences between artificial analogues and natural systems can be frequently overcome by ecological engineering to make the environment more suitable for native biodiversity, and/or assisted dispersal to allow suitable native organisms to reach appropriate sites within artificial ecosystems. Altering some habitats to become less analogous may help reduce impacts of pest species in urban and industrial areas.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".