Spatially explicit null models in biogeography: Toward a multi-scale understanding of the niche.
Bibliographic record
Abstract
In general, most ecologists envision the "niche" as a central organizing tenet, and that particular parameters of the niche help structure biogeographic patterns of diversity, distribution and abundance. The major emergent alternative to the niche concept requires the inference of background stochasticity, and its application through null models. For example, rather than competitive interactions of species shaping the coexistence of species, "historical accidents of dispersal" have been suggested. In this thesis I explore, in some detail, the concept of niche using of null models. In this thesis, two detailed and quite different null models are presented. The first, based on the "Mid-Domain Effect" (MDE), explores the influence of continental geometry on patterns in species richness and range size frequency distributions. I compared the MDE predictions first to observations on tree species richness in continental North America (n = 417 species), and then to amphibian, bird and mammal species richness across North and South America (n = 2216, 3771 and 1605 species, respectively) contrasting the relative contributions of null model results and environmental correlates. I have developed a novel null methodology to predict the niche of a species, or a group of species; I applied this at local and regional scales to examine null spatial distribution predictions for a single, endangered species at the local scale ( Opuntia humifusa at Point Pelee National Park), and for groups of rare species at a regional scale (based on reported occurrences across south-western Ontario). Results can be regarded as representing intermediate states between the extremes of continua of which niche and neutral models form the ends. With respect to the relative strengths of stochastic and deterministic processes, this thesis has characterized the attributes of groups of species. For example, large-ranged NA tree species are influenced by the MDE more than small-ranged species; moreover, regional, null species distribution models performed best for birds, insects, reptiles, sedges, as well as for aquatic and terrestrial plants. It seems most likely that real species distributions are the product of variation in relative strength of stochastic and deterministic processes.Dept. of Biological Sciences. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .V36. Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3559. Thesis (Ph.D.)--University of Windsor (Canada), 2006.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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 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".