Spatial and temporal scale-dependent feedbacks govern dynamics of biocrusts in drylands
Bibliographic record
Abstract
Abstract Biota could be ecosystem engineers in generating an intrinsic heterogeneous landscape through scale-dependent feedbacks. Thereby, they can form resource enriched patchiness or islands of fertility, comprising of self-organizing spatial patterns. Research so far has largely focused on the self-organized spatial patterns of plant communities in drylands. It, however, remains unclear whether and how biocrusts having distinct morphology and life history from plant communities could self-organize themselves and form unique spatial patterns. Here we conducted field observations of biocrusts across successional stages and employed a probabilistic Cellular Automaton (CA) model to investigate the distinct self-organized spatial patterns exhibited by mosaic patches of mosses and lichens with different patch size distributions (PSDs). Our study demonstrates that short-range positive feedbacks initially promote the development of patches, featured with a heavy-tailed PSD, while long-range negative feedbacks subsequently curtail further expansion of big patches, thereby establishing a characteristic patch scale with regular PSDs. Strikingly, only lichens reverted back to the heavy-tailed PSD in the late succession stage, presumably implying self-organized critical fragmentation of lichen patches. Field measurements of biocrust performance at the center and edge of patches of varying sizes along succession stages further support the classic scale-dependent feedback mechanism for Turing pattern formation. Collectively, our results clearly demonstrate the capability of the biocrust communities to self-organize themselves to form distinct spatial patterns governed by the spatial and temporal scale-dependent feedbacks, potentially impacting dryland ecosystem functions and resilience. Significance Statement An intriguing phenomenon is the capability of biota to form striking self-organized spatial patterns. Biocrusts serve as the soil’s skin and have different morphology and life history as compared to plant communities. We investigated these distinct self-organized spatial patterns exhibited by mosaic patches of mosses and lichens across succession stages through field surveys and a probabilistic Cellular Automaton (CA) model. Our results provide empirical evidence that biocrusts act as ecosystem engineers, forming self-organized spatial patterns. Simulations and field measurements of biocrust performance elucidate the mechanism of spatial and temporal scale-dependent feedbacks in governing biocrust spatial pattern formation. This self-organizing ability of biocrusts holds significant implications for ecosystem functions and the resilience of dryland ecosystems.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".