Drivers of Spatio-temporal Dynamics in Long-term Studies of Mammal and Bird Populations in Maine
Bibliographic record
Abstract
Investigating the dynamics of animal populations across large spatial and long temporal scales is fundamental to fully comprehend complex ecosystem processes since animals are responsible for many vital ecological functions including seed dispersal and vegetation regeneration. Spatial and temporal trends are particularly important in a changing world, where land-use and climate change can dramatically affect species distributions and interactions. Therefore, understanding how global change modifies populations’ structure in space and time is crucial for developing efficient conservation actions. The goal of my dissertation is to examine the drivers of spatio-temporal distribution patterns and demographic parameters of mammal and bird populations in the context of global change. My research advances our knowledge on this topic through five case-studies encompassing multiple taxa and ecological scales in the temperate forests of Maine. Chapter 1 investigates the long-term capability of small mammals (white-footed mice [Peromyscus leucopus], southern red-backed voles [Myodes gapperi], eastern chipmunks [Tamias striatus], and American red squirrels [Tamiasciurus hudsonicus]) to track and exploit pulsed resources, characterized by food resources that dramatically change in availability over space and time (e.g. mast-seeding) and its cascading effect on habitat selection and survival by using a 39-year capture-mark-recapture dataset. Chapter 2 examines the causes and consequences of the temporal increase in abundance and body weight of white-footed mice by using the same dataset as Chapter 1. Chapter 3 reports a laboratory experiment that investigates how white-footed mice and deer mice (Peromyscus maniculatus) differ in their acorn foraging behavior and its implications for oak distributions under climate change. Chapter 4 identifies how Canada lynx (Lynx canadensis) occupancy is influenced by forest composition and disturbance over 16 years throughout Maine. Finally, Chapter 5 describes how the functional diversity of mammal and bird communities is related to different forestry practices by using a 4-year dataset from Vermont, New Hampshire, and Maine. This dissertation elucidates some key mechanisms behind the spatial-temporal dynamics of multiple animal species, by combining large-scale, long-term empirical data across taxa with advanced modeling methods, and thus are of broad applicability to ecology and conservation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".