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Building a Regionally Connected Reserve Network in a Changing and Uncertain World

2010· article· en· W1584035796 on OpenAlexaff
Daniel Spring, Jiri Baum, Ralph Mac Nally, Michael MacKenzie, Arturo Sánchez‐Azofeifa, James R. Thomson

Bibliographic record

VenueConservation Biology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsUniversity of Alberta
FundersAustralian Research Council
KeywordsHabitatEnvironmental resource managementGeographyNature reserveWildlife corridorEcologyComputer scienceEnvironmental science

Abstract

fetched live from OpenAlex

Habitat connectivity is required at large spatial scales to facilitate movement of biota in response to climatic changes and to maintain viable populations of wide-ranging species. Nevertheless, it may require decades to acquire habitat linkages at such scales, and areas that could provide linkages are often developed before they can be reserved. Reserve scheduling methods usually consider only current threats, but threats change over time as development spreads and reaches presently secure areas. We investigated the importance of considering future threats when implementing projects to maintain habitat connectivity at a regional scale. To do so, we compared forward-looking scheduling strategies with strategies that consider only current threats. The strategies were applied to a Costa Rican case study, where many reserves face imminent isolation and other reserves will probably become isolated in the more distant future. We evaluated strategies in terms of two landscape-scale connectivity metrics, a pure connectivity metric and a metric of connected habitat diversity. Those strategies that considered only current threats were unreliable because they often failed to complete planned habitat linkage projects. The most reliable and effective strategies considered the future spread of development and its impact on the likelihood of completing planned habitat linkage projects. Our analyses highlight the critical need to consider future threats when building connected reserve networks over time.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.272
Teacher spread0.254 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations20
Published2010
Admission routes1
Has abstractyes

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