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Ubi es, room to roam? Extension of the LPB-RAP model capabilities for potential habitat analysis

2024· article· en· W4405894933 on OpenAlexaff
S. Holler, Kimberly R. Hall, Bronwyn Rayfield, Galo Zapata‐Ríos, Daniel Kübler, Olaf Conrad, Oliver Schmitz, Carmelo Bonannella, Tomislav Hengl, Jürgen Böhner, Sven Günter, Melvin Lippe

Bibliographic record

VenueEcological Modelling · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsCarleton University
FundersUniversität Hamburg
KeywordsExtension (predicate logic)HabitatGeographyEcologyComputer scienceBiology

Abstract

fetched live from OpenAlex

• Dynamic, long-term simulation-based analysis of ecological connectivity. • Comparison between SSP-RCP projection and potential FLR measures implementation. • For smallholder-dominated forest landscapes in spatiotemporal high-resolution. • For landscape planning and policy development towards mitigation and adaptation. The Anthropocene presents challenges for preserving and restoring ecosystems in human-altered landscapes. Policy development and landscape planning must consider long-term developments to maintain and restore functional ecosystems, ideally by using wildlife umbrella species as proxies. Forest and Landscape Restoration (FLR) aims to support both environmental and human well-being. However, the impact of FLR on wildlife umbrella species and their movement potential should be assessed in its potential magnitude for effective conservation. For this purpose, we introduce the LPB-RAP model expanded for potential habitat analysis in smallholder-dominated forest landscapes. It focuses on ecosystem fragmentation and landscape connectivity using Circuit Theory-based methods. LPB-RAP, based on a Monte Carlo framework, enables comprehensive habitat analysis for different SSP-RCP and policy scenarios with a broad analysis spectrum for anthropo- and biosphere aspects. It simulates dichotomous landscapes with and without potential FLR for consideration in long-term planning horizons. As an implementation example serves Ecuador's Esmeraldas province, using the Jaguar ( Panthera onca ) as a target umbrella species within an SSP2-RCP4.5 narrative. The simulation period covers 2018 to 2100 in annual and hectare resolution. The years 2024 and 2070 were chosen as probing dates for the extended habitat analysis. Results indicate that an agroforestry-based FLR scenario to increase forest cover while benefiting forest ecosystems and people would only marginally improve the movement potential for female Jaguars due to their avoidance of human-disturbed areas. Additional measures, including habitat corridors, are needed to enhance movement potential amidst increasing habitat fragmentation and loss, including stakeholders of all scales.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

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

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 head0.025
GPT teacher head0.249
Teacher spread0.224 · 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 designSimulation or modeling
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

Citations2
Published2024
Admission routes1
Has abstractyes

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