Density‐dependent plasticity in territoriality revealed using social network analysis
Bibliographic record
Abstract
Territories are typically defined as spatially exclusive areas that are defended against conspecifics. Given the spatial nature of territoriality, it is inherently density dependent, but the economics of territoriality also depend on the distribution and abundance of defended resources. Our objectives were to assess the effects of changing population density and food availability on individually based territorial phenotypes. We developed a novel analytical framework that bridges spatially explicit territories with social network analysis to model density-dependent territorial phenotypes. Using the outputs from our data pipeline, we modelled plasticity in territory size and territory intrusion rates in a long-term study population of North American red squirrels Tamiasciurus hudsonicus. Red squirrels defend year-round territories around a central hoard (midden) of white spruce Picea glauca cones. Importantly, white spruce is a masting species that produces large cone crops every 4-7 years (i.e. mast years) in our study area interspersed with non-mast years when few cones are produced. In the spring following mast years, populations are approximately double in size, but are lower in the spring of non-mast years. We predicted that territory size and intrusion rates would decrease as resource abundance, and consequently population density, increased. By contrast, as resource abundance decreased via depletion, and therefore density decreased, territories should increase in size and intrusions should also increase. As we expected, individual territory size and territorial intrusions were negatively density dependent, such that increased density after mast years resulted in smaller territories and fewer intrusions. When considering between-individual variation in plasticity across a density gradient, individuals responded differently to changes in population density within their lifetime. Our results show that territory size and intrusion rates display negative density dependence. When food becomes available in the autumn of a mast year and density in spring of the following year increases, territories shrink in size to effectively a small area around the midden. While our findings for red squirrels are unique compared to other systems, they serve as a reminder that the direction and strength of fundamental ecological relationships can depend on the nature of the system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".