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Record W2492015484 · doi:10.1017/cbo9781139627078.011

Behavior-based contributions to reserve design and management

2016· book-chapter· en· W2492015484 on OpenAlexaff
Colleen Cassady St. Clair, Rob Found, Aditya Gangadharan, Maureen H. Murray

Bibliographic record

VenueCambridge University Press eBooks · 2016
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeographyNature reserveHabitatCategorizationEnvironmental resource managementNatural (archaeology)EcologyUmbrella speciesConceptual modelEnvironmental planningComputer scienceEnvironmental scienceBiologyEndangered speciesArchaeology

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.004

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.

Opus teacher head0.021
GPT teacher head0.209
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

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