Multi‐scale analysis of plant species richness in Serengeti grasslands
Bibliographic record
Abstract
Abstract Aim To assess scale dependence between environmental factors and plant species richness. Additionally, we aimed to identify the scales at which niche relations and habitat heterogeneity, as hypothesized by A. Shmida & M.V. Wilson (1985) Journal of Biogeography , 12 , 1–20, operate in the savanna grasslands that were the focus of this study. Location Savanna grassland plant communities of Serengeti National Park, Tanzania. Methods Plant species richness was sampled in 102 modified Whittaker plots and tested for associations with two climate factors, mean annual rainfall (MAP) and potential evapotranspiration (PET), and two landscape variables, plot aspect (ASP) and topographic variation (TOPO), using multiple regressions. Scale dependence was assessed by conducting regressions after altering three aspects of spatial scale: grain, extent and focus. Grain was altered by analysing plant richness at 1, 10, 10 2 and 10 3 m 2 ; extent was investigated by restricting the maximum distance between samples to 75, 100, 125 and 150 km; and focus was manipulated by averaging samples spatially according to geographical land regions. Within the context of our data, we assumed that niche relations were represented by climate factors and habitat heterogeneity by landscape factors. Results Across all 102 plots, plant species richness between 1 and 10 2 m 2 had a negative relation to PET and a weak positive relation to MAP. Plant species richness at 10 3 m 2 had a positive association with TOPO and weaker associations with climate factors. ASP stayed in the model between grains of 10 and 10 3 m 2 , but had a very weak positive association with richness. When the focus was changed to land regions, associations between plant species richness and explanatory variables strengthened, but were not qualitatively different. At spatial extents of 75 and 100 km, PET was the strongest correlate of plant species richness across all spatial grains. At spatial extents ≥ 125 km, PET explained the majority of the model variance at spatial grains ≤ 10 2 m 2 , whereas TOPO explained equal amounts or more of the model variance at spatial grains of 10 3 m 2 . Main conclusions Both climate and topographic variation explained plant species richness in Serengeti grasslands, but specific patterns depended on grain, extent and, to a lesser degree, focus. Consistent with the ideas of Shmida & Wilson (1985) , determinants of plant species richness shifted from niche relations to habitat heterogeneity between spatial grains of 1 and 10 3 m 2 , although this occurred only at relatively large spatial extents (≥ 150 km). Finally, the signs, strength and shape of plant species richness relationships in Serengeti closely match those that describe macro‐scale patterns of woody plant species richness across the entire African continent.
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 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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".