Geographic variation in genetic and demographic performance: new insights from an old biogeographical paradigm
Bibliographic record
Abstract
The 'centre-periphery hypothesis' (CPH) is a long-standing postulate in ecology that states that genetic variation and demographic performance of a species decrease from the centre to the edge of its geographic range. This hypothesis is based on an assumed concordance between geographical peripherality and ecological marginality such that environmental conditions become harsher towards the limits of a species range. In this way, the CPH sets the stage for understanding the causes of distribution limits. To date, no study has examined conjointly the consistency of these postulates. In an extensive literature review we discuss the birth and development of the CPH and provide an assessment of the CPH by reviewing 248 empirical studies in the context of three main themes. First, a decrease in species occurrence towards their range limits was observed in 81% of studies, while only 51% demonstrated reduced abundance of individuals. A decline in genetic variation, increased differentiation among populations and higher rates of inbreeding were demonstrated by roughly one in two studies (47, 45 and 48%, respectively). However, demographic rates, size and population performance less often followed CPH expectations (20-30% of studies). We highlight the impact of important methodological, taxonomic, and biogeographical biases on such validation rates. Second, we found that geographic and ecological marginality gradients are not systematically concordant, which casts doubt on the reliability of a main assumption of the CPH. Finally, we attempt to disentangle the relative contribution of geographical, ecological and historical processes on the spatial distribution of genetic and demographic parameters. While ecological marginality gradients explain variation in species' demographic performance better than geographic gradients, contemporary and historical factors may contribute interactively to spatial patterns of genetic variation. We thereby propose a framework that integrates species' ecological niche characteristics together with current and past range structure to investigate spatial patterns of genetic and demographic variation across species 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 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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".