Partitioning the variation in African vertebrate distributions into environmental and spatial components – exploring the link between ecology and biogeography
Bibliographic record
Abstract
There has been a proliferation of studies aimed at predicting the distributions of species from environmental variables despite evidence that spatial interpolation or spatially‐constrained mechanistic models have comparable explanatory power. Moreover, the processes behind environmental and spatial correlations – and their interactions – remain elusive. Here, we examined geographic patterns in the amount of variation explained by environmental correlation and exogenous or endogenous spatial autocorrelation for 4423 terrestrial vertebrate species in Africa using variation partitioning analysis. We also tested the effects of range size and taxonomic class on the relative importance of environmental and spatial correlations, and contrasted empirical patterns to two environmentally‐neutral models to identify potential underlying environmental and spatial mechanisms. Results showed that geographic range size was associated with environmental and spatial variation components in ways that where qualitatively indistinguishable from environmentally‐neutral species with constrained dispersal, suggesting that proportions of variation are due to range cohesiveness rather than other ecological processes. As a consequence, large‐scale patterns of biodiversity should be studied cautiously due to the difficulty of obtaining evidence of causal mechanistic links between species distributions and spatio‐environmental gradients. However, we also uncovered ecologically‐meaningful patterns in the residuals of the relationship between range size and the respective variation components, which differed among vertebrate classes. Moreover, these patterns coincided with contemporary biogeographical regions. This study, therefore, demonstrates that it is possible to extract meaningful environmental and spatial associations that potentially link ecological and biogeographical processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".