Modelling British Columbia’s ecosystems and avian richness using landscape-scale indirect indicators of biodiversity
Bibliographic record
Abstract
Developing consistent and repeatable broad-scale methods for biodiversity modelling is an important goal to address as habitat loss, fragmentation and environmental degradation threaten our ability to maintain ecosystem and species diversity levels. Geospatial reviews of biodiversity monitoring have identified ecological indicators for the indirect mapping of species richness and ecosystem components modelling the processes controlling species distribution gradients. The goal of our research is to advance broad-scale biomonitoring by demonstrating how landscape-scale environmental indices can be used to model regional ecosystem and species diversity of British Columbia (BC), Canada. We meet our ecosystem-modelling goal by selecting and developing suitable ecological indicators from Earth observation data and terrain indices to represent the structure, composition and function of the environment, displaying both static and dynamic landscape processes of BC’s ecosystems. We regionalize the selected indirect indicators of biodiversity using a two-step clustering algorithm. The results display 16 ecologically distinct terrestrial ecosystems, 10 of which characterize the northern Boreal, coastal and Southern Interior mountain regions, and six represent the coastal lowlands, interior, Georgia Depression, Boreal and Taiga Plains of British Columbia. Comparing our classification to BC Ministry of Forests biogeoclimatic zone mapping, we find spatial similarity in the coastal, Taiga and Boreal Plains. Overall, our classification distinguishes a greater diversity of ecosystems in the mountainous regions of the province and greater homogeneity in the Central Interior where our landscape characteristics represent current productivity conditions. Our approach to ecosystem modelling supports legacy mapping by providing ecological information in under-sampled regions of BC and offers a method for consistent repeat modelling of ecosystem diversity to identify landscape change. To meet our species-modelling goal we employ a flexible non-parametric regression tree model (Random Forests) to establish the power of landscape-scale indicators (productivity, ambient energy, and heterogeneity) to predict the spatial distribution of breeding bird richness and establish the dominant landscape processes controlling vertebrate richness throughout BC. Our models explain approximately 40% of the variation in survey effort stratified breeding bird species richness levels and distinguish ambient energy as the top ranked environmental predictors of breeding richness. Using our modelled relationships, we forecast breeding richness levels for the regions of BC not currently surveyed to support conservation management of birds and vertebrate species. The results identify the lowland, warm and dry regions of the Boreal, Taiga, South and Central Interior and the Georgia Depression to be species rich. These results have implications for conservation managers, as high breeding richness is also concentrated in the areas favourable to human settlement. Additionally, by connecting breeding bird data derived from remotely sensed data and continuously collected climate data, we provide an approach for monitoring ecological indicators as surrogates of vertebrate population levels over broad spatial scales.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".