Functional implications of land use and climate change on bird assemblages worldwide
Bibliographic record
Abstract
Human well-being and sustainable development depend on ecosystems delivering contributions to people now and in the future. However, pervasive anthropogenic pressures on biodiversity threaten ecosystems’ potential to perform essential processes and functions. Understanding functional implications of human-driven biodiversity change worldwide, and how they might be mitigated, is therefore an urgent research priority. Functional diversity – the diversity and distribution of functional traits – can better predict ecosystems’ functional resilience to environmental perturbations than species richness. Trait-based approaches may support achieving global biodiversity targets to reverse biodiversity loss and enhance ecosystem functioning, such as the Kunming-Montreal Biodiversity Framework (KMGBF). In this thesis, I collate, integrate and curate large global datasets of bird assemblage composition, population trends and functional traits to assess the implications of land-use and climate change for bird assemblages. I first demonstrate the potential of functional trait approaches in measuring ecosystem integrity, a key component of KMGBF’s Goals A and B. I show that human modification of natural habitats erodes ecosystem integrity by driving contraction, shifts and internal erosion of avian trait space, with impacts varying across groups and associated processes. Second, I address the lack of a biodiversity indicator based on functional trait data by proposing and prototyping the Functional Intactness Index (FII). FII is a model-based indicator that estimates the degree to which an ecological assemblage has retained functional diversity expected in the absence of human impacts. I make global projections of FII in 2000 and 2020 that may identify regions of conservation priority. Finally, I show species’ functional traits influence their population responses to anthropogenic pressures in North America. Mechanisms underpinning population change arise from interactions between species’ traits and exposure to environmental pressures, highlighting the need for a holistic approach to conservation decision-making considering this context-dependency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".