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Enregistrement W2902120842 · doi:10.1002/bes2.1485

Diversifying Ecology by Bridging Western Science and Traditional Ecological Knowledge

2018· article· en· W2902120842 sur OpenAlexaboutno aff
Cristina Eisenberg

Notice bibliographique

RevueBulletin of the Ecological Society of America · 2018
Typearticle
Langueen
DomaineHealth Professions
ThématiqueIndigenous Studies and Ecology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEcologySubsistence agricultureGeographyTraditional knowledgeWildlifeArcticHistorical ecologyBiodiversityArctic ecologyEnvironmental resource managementIndigenousAgricultureBiologyEnvironmental science

Résumé

récupéré en direct d'OpenAlex

Review of Jordan Hoffman, Avatimik Kammattiarniq: Arctic Ecology and Environmental Stewardship. Nunavut Arctic College Media, Iqaluit, Nunavut, Canada, 2018. A pressing need exists to increase diversity and inclusion in the sciences. In recognition of how the science of ecology is evolving and transforming and of the need to incorporate diverse interdisciplinary and eco-cultural perspectives into ecology, the Ecological Society of America (ESA) has chosen as the theme of its 2019 annual meeting “Bridging Communities and Ecosystems: Inclusion as an Ecological Imperative.” Traditional Ecological Knowledge (TEK), defined in the early 1990s as knowledge and practices passed orally across generations informed by cultural memories, sensitivity to change, and reciprocity (Kimmerer 2000), is an important field gaining recognition in ecology. TEK land-care processes include modifying vegetation to improve wildlife habitat for hunting, adjusting hunting and gathering practices for subsistence species of wildlife, such as caribou (Rangifer tarandus), and plants such as serviceberry (Amelanchier alnifolia), and creating disturbances, such as fire. For millennia, Indigenous people globally have used such practices to increase ecosystem productivity. TEK practices can increase biodiversity and ecological resiliency by creating fine-grained, patchy landscape mosaics (Turner et al. 2000). TEK offers a web of knowledge that includes values that can help restore ecosystems (Lightfoot et al. 2013). Ecologists today have been working to use TEK to understand better how ecosystems function, and how ecosystems and the species they contain can be more resilient to climate change. For instance, in the last two decades in the North American Arctic, the Inuit People have been weaving TEK into innovative ecological studies and natural-resources management plans, by working collaboratively with federal managers and ecologists, and by educating the next generation of Indigenous scientists. To support such collaboration and empowerment, Nunavut Arctic College Media has been publishing a series of books and multi-media material that function as textbooks, guides, and essential resources for Indigenous and non-Indigenous ecologists, practitioners, and students. One of the latest offerings in this series, Avatimik Kammattiarniq: Arctic Ecology and Environmental Stewardship, by Jordan Hoffman, provides a much-needed contribution to this field. The phrase “Avatimik Kammattiarniq,” which means “respect and caring for the land, animals, and the environment,” sets forth the societal values codified in the Nunavut Wildlife Act that this book explores as part of Inuit Qaujimajatuqangit, or “Inuit IQ,” defined as Inuit TEK. Inuit IQ means survival; to wit, Section 1(f) of the Nunavut Wildlife Act calls for Avatimik Kammattiarniq—mutual respect and treating nature holistically to guide the government and people of Nunavut in making decisions about natural resources. This means, for example, implementing wildlife management practices that complement Inuit harvest rights (Government of Nunavut 2013). But such implementation can only work by bridging Western science and TEK. Hoffman's thesis throughout his book is that we need to understand the relationships between living organisms and the non-living world in any given environment to make informed decisions about natural resources. He has written this book to address this need, organizing it into eight chapters (“Ecology: the study of relationships in nature,” “Energy flow and food webs,” “Interactions among organisms,” “Populations,” “Biomes,” “Lake ecology,” “Snow ecology,” and “Evolution and natural selection”), which cover classic topics in ecology through both Western science and TEK lenses. Each chapter has an introductory section, which includes a list of key terms, followed by helpful learning-check questions for each chapter sub-section, review questions, and activities for readers. The author aims to describe Arctic ecology based on foundational Western science principles and from an Inuit perspective. As a practicing ecologist who is Indigenous and works in ecosystems around the world, I have found that regardless of where one looks (e.g., Mongolia, Kenya, Amazonia), if one were to draw a Venn diagram of these two philosophical ways of looking at ecosystems, one would find a near-total overlap. Hoffman explores this common ground by examining relationships among biotic and abiotic components of Arctic ecosystems, while considering climate-change impacts and environmental stewardship in a rapidly changing world. He grounds this material in Inuit ways of knowing about the natural world, encouraging readers as they read to talk with community members in order to make the vital connections needed within a community to advance environmental stewardship. Hoffman presents the Western science in this book, which consists of the foundational tenets of ecology, in a straightforward, clear manner, adhering closely to established ecological conventions. He covers topics that include ecological niches, competition, thermodynamics, population dynamics and regulation, biomes, eutrophication and energy cycles in lakes, species adaptations, genetics, and evolution. However, he uses case studies to take this book into a more holistic realm, one that bridges Western science and TEK. Case studies consist of interviews with or quotes from eminent scientists and tribal elders (and sometimes individuals who function as both) that address the topic of each chapter in an integrative manner. He accompanies these case studies with questions for deeper thought, encouraging readers to do some introspection on these questions, and also to discuss them with their friends, peers, and community members. For example, he opens the book with classic definitions of fundamental ecological concepts such as the carbon cycle and energy flow through ecosystems—and then uses an interview with an Inuit elder to illustrate how these concepts apply to climate change and Inuit IQ. The quality of the production of this book is superb. It contains dozens of compelling photographs taken in the Arctic that illustrate ecological principles in a manner to which readers who may be Inuit young adults will strongly relate. In combination with the case studies, this approach makes this book widely accessible and a very effective vehicle for teaching, learning, and finding necessary solutions. I highly recommend this book to anyone seeking to understand better how ecological principles apply to the Arctic; to Indigenous students striving to become ecologists while adhering to their culture; to anyone who works in the Arctic in natural-resources management and conservation; and to anyone who has an interest in climate change. This book functions well as a college textbook. And it belongs on the bookshelf of anyone who cares about the Arctic and how climate change is affecting communities there.

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)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesÉtudes des sciences et des technologies
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,410
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0050,006
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,050
Tête enseignante GPT0,328
Écart entre enseignants0,277 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

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

Citations3
Publié2018
Routes d'admission1
Résumé présentoui

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