Behavior-based contributions to reserve design and management
Bibliographic record
Abstract
INTRODUCTION All students of conservation are familiar with the quintessential model of a reserve network, in which a hostile, human-dominated matrix limits the occurrence of natural habitat and vulnerable species to scattered protected areas connected by corridors of intermediate suitability (e.g. Diamond 1975, Soule & Terborgh 1999, Bennett 2003). This conceptual model also identifies anthropogenic features, such as roads, that may create such significant barriers to animal movement that they require mitigation (reviewed by Forman et al. 2006). The resulting construct for conservation planning tends to categorize types of space as core areas, corridors, matrix and barriers while underestimating the myriad non-spatial features of both species and landscapes that exist along inconvenient and intersecting continua. Behavior is one of these factors and it contributes much to the fate of imperiled populations, but its effects have not been much synthesized in the contexts of reserve design and conservation management. Before delving into the role of behavior in reserve design, it is worth pausing to consider some of the reasons for the traditional emphasis on spatial characteristics. First, binary and spatial constructs are readily visualized by people to facilitate common and explicit goals, such as the creation of national parks and other kinds of protected areas. Second, spatial features of reserve design are supported by foundational and extensive ecological theory, much of which emanated from Island Biogeography (MacArthur & Wilson 1967, reviewed by Lomolino & Brown 2009), to provide support for conservation predictions, management actions and enduring academic interest. A third reason that spatial attributes lead so much of reserve design is that space influences most of the physical experiences of organisms and defines most anthropogenic threats to biodiversity (Chapter 1) across a vast range of scales. Despite the good reasons to emphasize space in reserve design, we contend that space alone does not define the experience of any individual or directly imperil populations. Space is more like a canvas on which those experiences play out. Protecting the habitat contained in space is essential to most conservation action, but that action alone cannot ensure the survival of individuals, populations, species or ecosystems. Moreover, spatial attributes are difficult to generalize as both problems and solutions in conservation (Newmark 1996, Gascon et al . 2000), which limits their proactive use in ways that could best advance conservation goals (Caughley 1994).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".