Spatio‐temporal connectivity: assessing the amount of reachable habitat in dynamic landscapes
Bibliographic record
Abstract
Summary Landscape heterogeneity and habitat connectivity affect species movements, playing an important role in determining the likelihood of species persistence. However, landscape connectivity is usually evaluated using static snap‐shots, which do not account for the sequential interactions among habitat patches through time. We developed a network‐based model of landscape dynamics, and corresponding connectivity metrics, to account for the reachable habitat across space and time. We illustrate the behaviour of these metrics, using fragmented forested landscapes in the Atlantic Forest of Brazil. We parametrized the models using the dispersal capacities of selected bird and small mammal species. We found that when considering spatio‐temporal links, connectivity is estimated to be on average 30% higher (with a maximum of 150% higher) than what is estimated from purely spatial models. This higher degree of spatio‐temporal connectivity arises due to connections through temporal stepping‐stone patches that appear (habitat gain) and disappear (habitat loss) over time. Species with short dispersal distances (<1000 m) particularly benefited from the spatio‐temporal connections. The contribution of spatio‐temporal connectivity to habitat reachability increased with higher habitat loss rates. Moreover, it depended on the amount of habitat in the landscape, being higher at intermediate habitat amounts (∼30%). We showed that accounting for spatio‐temporal connectivity is critical for understanding ecological patterns and processes in dynamic landscapes, and that a series of purely spatial connectivity metrics underestimates the actual connectivity patterns across time. The proposed spatio‐temporal connectivity approach and metrics can be applied to evaluate the effective connectivity patterns and trends in a variety of dynamic landscapes, avoiding the potential overestimates of population isolation and extinction probabilities that may result from widely used purely spatial connectivity models.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".