Systematic Map of Urban Connectivity Research Reveals a Dearth of Validation of Connectivity Estimates
Bibliographic record
Abstract
Purpose of Review: Increasing ecological connectivity among urban greenspaces is a global conservation priority to protect urban wildlife. However, effective monitoring remains a challenge, as connectivity models are rarely validated against biological data despite the need for these models to represent real wildlife movement. Here, we systematically reviewed the urban connectivity literature to assess (i) the extent of connectivity model validation; (ii) how validation varies by study objectives; (iii) where urban connectivity research is conducted; and (iv) what types of connectivity metrics, taxa, and biological data are used. Recent Findings: Of the 430 studies reviewed, nearly half validated their connectivity models using biological data, but few used movement data. Structural connectivity metrics dominated, although use of functional metrics has increased in recent years. A clear taxonomic bias was also evident, with a disproportionate focus on birds. When validation was conducted, most relied on species richness or other biodiversity metrics. Such approaches offer ambiguous evidence for actual connectivity, as biodiversity patterns are often influenced by confounding factors like greenspace size and the speciesarea relationship. As such, direct empirical support for connectivity models capturing wildlife movement remains limited. Summary: Urban connectivity models are often applied without clear evidence that they represent actual ecological processes. To address this, future studies should incorporate a broader range of taxa and test multiple model types to disentangle how movement patterns align with different connectivity frameworks. Integrating biological validation, particularly movement data, into connectivity modelling is essential to tracking progress toward global goals for ecologically resilient cities. Supplementary Information: The online version contains supplementary material available at 10.1007/s40823-025-00106-y.
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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 | 0.012 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".