Flexible movement kernel estimation in habitat selection analyses with generalized additive models
Bibliographic record
Abstract
Abstract Habitat selection analysis includes resource selection analysis (RSA) and step selection analysis (SSA). These frameworks are used in order to understand space use of animals. Particularly, the SSA approach specifies the space availability of sequential locations through a movement kernel. This movement kernel is typically defined as the product of independent parametric distributions of step lengths (SLs) and turning angles (TAs). However, this assumption may not always be plausible for real data where short SLs are often correlated with large TAs and vice versa. The objective of this paper is to relax the need for parametric distributions using generalized additive models (GAMs) and the R-package mgcv, based on the work of Klappstein et al. (2024). For this, we propose to specify the movement kernel as a bivariate tensor product, rather than independent distributions of SLs and TAs. In addition, we account for residual spatial autocorrelation in this GAM-approach. Using simulations, we show that the tensor product approach accurately estimates the underlying movement kernel and that the fixed effects of the model are not biased. In particular, if the data are simulated with a copula distribution for SL and TA, i.e. if the independence assumption for SL and TA does not hold, the GAM approach produces better estimates than the classical approach. In addition, including a bivariate tensor product in the model leads to a better uncertainty estimation of the model parameters and a higher predictive quality of the model. Incorporating a bivariate tensor product solves the problem of assuming parametric distributions and independence between SLs and TAs. This offers greater flexibility and makes the analysis of real data more reliable.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".