Bibliographic record
Abstract
Of the many factors that affect population dynamics and persistence, landscape heterogeneity is increasingly recognized as one important factor that is particularly relevant to biodiversity conservation and management. However, understanding interactions between mobile species and landscape heterogeneity remains an outstanding challenge in ecology. Studies considering the influence of landscape heterogeneity on population dynamics are less common, and detailed knowledge of the relationship between landscape heterogeneity and population dynamics in patchy habitats is very poor. This study tested how landscape heterogeneity affects population behaviour and persistence in patchy environments, including (1) relative influences of patch geometry and within-patch heterogeneity, (2) effects of spatial patterns of within-patch heterogeneity on patchy populations, (3) landscape spatial structure and species’ life-history traits, and (4) differential roles of structural and functional landscape heterogeneity. The results presented in this thesis demonstrate that spatial heterogeneity within habitat patches, together with patch area, controls population abundance of the habitat specialists, but had little influence on generalist species. Populations of specialised species become less variable in size, and experience lower probability of extinction in landscapes with positively autocorrelated within-patch habitat quality. Increasing scale of spatial autocorrelation in habitat quality would greatly increase the size and mean resource share of virtual populations, and low-tolerant species were appreciably greater in size than high-tolerant species. Species’ movement capacity plays a critical role in shaping the increase in population size in response to increased spatial autocorrelation in habitat quality, where low-mobility species had a logarithmic-like increase in response to increased scale of spatial autocorrelation, contrasting with the exponential-like increase for high-mobility species. Such contrasting patterns of population increase in relation to movement capacity indicate that the effect of distance-based movement capacity may function in highly scale-dependent ways, and its relative strength is determined by the interaction of movement distance and the scale of spatial autocorrelation. Structural and functional properties of landscape heterogeneity can have different consequences on animal species. As we shown in the thesis, functional landscape heterogeneity influenced space use of black bears at intermediate scales (i.e., 1~2 km), whereas the impact of structural heterogeneity was most significant at the finest scale analysed (i.e., 200 m). The scale-dependent responses of animals to different forms of landscape heterogeneity indicate that an explicit separation of the effects of different landscape heterogeneity across scales is critical to our understanding of the spatial nature of animal-environment relationships. Summarizing, this study highlights the importance of the landscape heterogeneity in determining the dynamics and persistence of populations inhabiting patchy environments. Both the spatial pattern and extent of landscape heterogeneity can interact with fragmentation to greatly influence populations with different life-history traits. Ecologists need to be mindful of the simplifying assumptions of theories and resulting models and predictions in assessing the suitability of their application for specific species and landscapes.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".