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Reconciliation Ecology and the Future of Species Diversity

2005· book-chapter· en· W4235296609 on OpenAlexaboutno aff
Michael L. Rosenzweig

Bibliographic record

VenueOxford University Press eBooks · 2005
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsThreatened speciesSpecies richnessEcologyRange (aeronautics)Extinction (optical mineralogy)GeographyBiologyPaleontologyHabitat

Abstract

fetched live from OpenAlex

Alexander von Humboldt (1807) provided the first hint of one of ecology’s most pervasive rules: larger areas contain more species than do small ones. Many ecologists see that rule—the species–area relationship—as one of ecology’s very few general laws (e.g., Lawton 1999, Rosenzweig and Ziv 1999). In the past two centuries, ecologists have learned a lot about species–area relationships. I will explore that knowledge and show that we can already use it in the struggle to minimize extinction losses. It teaches us what proportion of diversity is truly threatened and how to prevent most losses by applying a new strategy of conservation biology. Olaf Arrhenius (1921) and Frank Preston (1960) formalized the species–area pattern by fitting it with a power equation: . . . S = Caz (1) . . . where S is the number of species, A is the area, and C and z are constants. For convenience, ecologists generally employ the logarithmic form of this equation: . . . log S = c + z log A (2) . . . where c = log C. (Note that I do not use the jargon term “species richness.” To understand why, see Rosenzweig et al. 2003.) The species–area power equation, or SPAR, can be fitted to an immense amount of data (Rosenzweig 1995). Ecologists are not sure why a power equation fits islands or continents. But we do have a successful mathematical theory for areas within a province. Brian McGill (personal communication) has deduced the species–area curve within provinces from four assumptions: • The geographical range of each species is independently located with respect to all others. • Species vary in abundance with respect to each other. • Species have a minimum abundance. • Each species’ abundance varies significantly across its own range, being relatively scarce more often than relatively common. (“Relatively” means with respect to its own average abundance.) Data support all four assumptions. From them, McGill shows that there is a species–area curve and that it approximates a power equation whose z-value ranges between 0.05 and 0.25 with a mean of about 0.15.

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)0.009
metaresearch head score (Gemma)0.017
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 score0.010
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0030.017
Scholarly communication0.0080.018
Open science0.0020.007
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0100.002

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 head0.011
GPT teacher head0.172
Teacher spread0.161 · 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

Citations6
Published2005
Admission routes1
Has abstractyes

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