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Enregistrement W2559131200 · doi:10.14264/uql.2016.969

Identifying critical habitat for threatened species: concepts and challenges

2016· dissertation· en· W2559131200 sur OpenAlexaboutno aff
Abbey E. Camaclang

Notice bibliographique

RevueThe University of Queensland · 2016
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueRangeland and Wildlife Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCritical habitatHabitatThreatened speciesEndangered speciesEnvironmental resource managementGeographyDocumentationEcologyUmbrella speciesHabitat destructionHabitat conservationExtinction (optical mineralogy)Environmental scienceComputer scienceBiology

Résumé

récupéré en direct d'OpenAlex

Critical habitat is defined scientifically as the subset of habitat necessary for the long-termpersistence of a given species. Based on this definition, loss of any part of the critical habitat wouldresult in extinction of the species. In the United States, Australia, and Canada, critical habitat ofthreatened species is protected, to various degrees, under endangered species legislation. Effectiveprotection of critical habitat depends on it being identified accurately. Where there is potential forconflict with landowners and industry stakeholders, accurate critical habitat identifications are moredefensible in court, and minimise the opportunity costs of protecting areas that may not be asbeneficial to species persistence. However, obtaining the data required to accurately identify criticalhabitat can take up considerable time and resources that may otherwise be spent on conservationactions. At the same time, delaying protection of critical habitat to improve knowledge can result infurther habitat loss. In this thesis, I review key concepts and challenges surrounding theidentification of critical habitat, and develop decision tools to assist in deciding when and how toidentify it.In Chapter 2, I present a systematic review of critical habitat documentation from the United States,Canada, and Australia to identify the types of data and criteria that have been used to identifycritical habitat in the last decade. Contrary to scientific recommendations that long-term speciespersistence should be used as the criterion for identifying critical habitat, information about thelocation of species occurrences and particular habitat features were used instead to identify criticalhabitats for most of the species reviewed. Insufficient data and the desire to avoid potentialopposition from landowners are likely to be the main reasons for the use of such approaches tocritical habitat identification. Chapter 3 continues with an examination of the merits of the differentcriteria that could be used to inform critical habitat identification, and the types of errors associatedwith each. I also considered the potential consequences of the errors, and recommended that moreexplicit recognition of the potential for errors is important in minimising their negativeconsequences for species persistence.While the accuracy of critical habitat identification may be improved by collecting more data,delaying protection to do so may result in additional habitat loss if habitats are left unprotected inthe meantime. In Chapter 4, I used an optimisation approach to examine this trade-off between thebenefits of delaying protection to improve accuracy and the costs of additional habitat loss in theinterim. I modelled the change in the proportion of habitat correctly identified over time as afunction of both accuracy and habitat loss, and determined the optimal amount of time to spend learning that maximises this value. I found that at low rates of habitat loss, slow learning ratesresulted in a longer optimal learning period. At high rates of habitat loss, however, the improvementin accuracy no longer compensates for the loss of habitat, and the optimal amount of time to spendlearning becomes less than three years, regardless of the learning rate.Given the need for prompt critical habitat protection to avoid additional habitat loss, how should weidentify critical habitat to maximise accuracy while minimising the amount of time and resourcesspent in the process? I addressed this question in Chapter 5 by describing how a structured decisionmakingframework can be applied to help decide which types of data and approaches should beused to identify critical habitat, based on the conservation objectives, movement and habitat usepatterns, and the amount of habitat currently available for the species. Using a structured decisionframework to guide critical habitat identification can also help improve the consistency andtransparency of identification, as well as increase confidence in the validity of critical habitatidentification.This thesis aimed to recommend improvements to the identification of critical habitat by reviewingcurrent practices and potential errors in critical habitat identification, examining the potentialconsequences of these errors for conservation, and developing decision tools to assist in decidingwhen and how critical habitat should be identified. However, it is important to note that adequateenforcement of critical habitat protection also plays a crucial role in ensuring long-term speciespersistence. Further studies to examine how critical habitat protections are implemented andenforced will help to evaluate the overall impact of critical habitat identification on the recovery ofthreatened species, and provide insight into how critical habitats can be protected more effectively.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,631
Score d'incertitude au seuil0,308

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,035
Tête enseignante GPT0,257
Écart entre enseignants0,222 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2016
Routes d'admission1
Résumé présentoui

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