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Enregistrement W6980617362

Climate change direct and indirect impacts on biodiversity in the Congo Basin: a multi-dimensional approach

2025· dissertation· en· W6980617362 sur OpenAlexaboutno aff

Notice bibliographique

RevueIRIS Research product catalog (Sapienza University of Rome) · 2025
Typedissertation
Langueen
DomaineComputer Science
ThématiqueResearch Data Management Practices
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRainforestDeforestation (computer science)BiodiversityClimate changeSierra leoneGlobal warmingHabitat destructionSwampThreatened species
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Spanning 2.87 million km2 across six Central African countries (Cameroon, the Central African Republic, the Democratic Republic of Congo, Equatorial Guinea, Gabon and the Republic of Congo), the Congo Basin is the world’s second-largest continuous tropical rainforest after the Amazon. Hosting diverse habitat types (e.g. savannas, terra firma, swamps or seasonally flooded forests), the Congo Basin is ranked third in terms of global species richness and support exceptional levels of biodiversity, including charismatic, endangered megafauna such as the African forest elephant (Loxodonta cyclotis), western lowland gorilla (Gorilla gorilla gorilla), central chimpanzee (Pan troglodytes troglodytes) or bonobo (Pan paniscus). Due to its vast carbon stocks, the region has global significance for climate regulation, storing approximately 29 petagrams of carbon in its peatlands alone, alongside substantial above-ground biomass in its forests. However, Congo Basin rainforests also face multiple threats, including deforestation and forest degradation driven by small and large-scale agriculture, and extractive industries such as logging, mining or oil production, alongside illegal wildlife trade. Climate change is expected to further exacerbate these pressures, with profound consequences for both biodiversity and Congo Basin human populations, many of whom depend on forest resources for subsistence. Despite its ecological and climatic importance, the region remains critically under-researched: a lack of long-term monitoring, extensive temporal and spatial gaps in instrumental data and the scarcity of field studies result in the frequent exclusion of the Congo Basin from regional and global climate and biodiversity assessments. Given the trajectory of other globally significant rainforests under climate change, it is imperative to investigate how climate change is impacting the Congo Basin and will continue to do so in the future. Addressing this critical knowledge gap, this PhD research aims to enhance the understanding of climate change’s direct and indirect impacts on biodiversity in the Congo Basin, using a multi-scalar and interdisciplinary approach. Direct impacts refer to, for example, shifts in species distributions, altered phenological patterns or disrupted trophic networks, while indirect impacts encompass human adaptation responses that may compound existing threats to biodiversity. This study is guided by three primary objectives: (1) to assess climate change impacts on biophysical systems across multiple spatial scales, (2) to identify local adaptation responses in response to climate change and their associated indirect effects on biodiversity, and (3) to evaluate climate change impacts on a keystone species, the African forest elephant. My first analytical chapter, entitled “Uncertain future for Congo Basin biodiversity: a systematic review of climate change impacts”, systematically reviewed current and projected climate change impacts on biodiversity at all organizational levels, from genes to ecosystems. The findings reveal widespread effects, including increased species vulnerability to extinction, shifts in species distributions and reductions in organism body size, while studies on species genetics, physiology, and phenology remain scarce. Research combining climate change with other drivers of change, such as land-use change or human adaption responses, projects a bleak outlook in terms of Congo Basin biodiversity persistence. However, substantial knowledge gaps remain, particularly concerning population dynamics, shifts in plant resource availability and large-scale compositional changes. This review underscores the urgent need for further research to address these deficiencies. In my second analytical chapter, ““We saw fish in the cocoa farm”: local observations of the impacts of climate change on biodiversity and livelihoods in the Lac Télé Community Reserve, Congo Basin”, I present findings derived from field data collection in the Lac Télé Community Reserve, a seasonally flooded site in the northern Republic of Congo. Across four villages spanning swamp to terra firma forests, 80 semi-structured interviews, 111 structured interviews and eight focus groups were conducted to document local climate change, impacts on biophysical systems and human adaptation responses, through the knowledge of local communities. Results indicate significant climate change impacts in this site, including increasing temperatures, shifting precipitation patterns and intensified flooding, as well as associated impacts on local biodiversity, such as increased tree mortality, disrupted fish reproduction or disrupted migratory patterns in mammal species. Local communities have implemented diverse adaptation responses, e.g. changes in fishing and agricultural practices, which may exert additional anthropogenic pressures on biodiversity. This chapter highlights the value of local knowledge as a complementary tool for ecological monitoring in data-deficient ecosystems and underscores the need to assess similar climate changes across other swamp forests and seasonally inundated sites. In my third research chapter, called “Shrinking climatic space: implications for forest elephant conservation in the Congo Basin under climate change”, I assessed climate change impacts on the African forest elephant, a Congo Basin keystone species playing a critical role in shaping forest structure and dynamics. While forest elephants face severe pressure from illegal wildlife trade and habitat loss, climate change acts as an emerging but understudied threat. I used a bioclimatic envelope modeling (a method used to predict the suitable climatic conditions for a species based on its known occurrences and associated bioclimatic variables) and an ensemble forecasting approach (which refers to the integration of multiple models or algorithms to improve prediction reliability) to assess shifts in climatic suitability for the forest elephants under a low (SSP 245) and high-greenhouse gas emissions (SSP 585) scenarios by 2100. Given sampling biases associated with occurrence data across the species’ range, I developed a two-approach framework to estimate species presence. The first standard approach utilized occurrences obtained from the Global Biodiversity Information Facility (GBIF), while the second approach employed a regular sampling of pseudo-presences within the species geographic range, as delineated by the IUCN Red List of Threatened Species. Projections reveal a consistent decline in suitable climatic conditions across datasets and scenarios, with contraction in climatic suitability in the central Congo Basin and increased fragmentation in West Africa. Critically, projections indicate that half of the current protected area network will lose climatic suitability. When accounting for future deforestation risk, the overlap between projected areas of climatic suitability and high deforestation pressure further exacerbates conservation challenges, underscoring an increasingly precarious outlook for the long-term persistence of forest elephant populations. These findings should inform conservation strategies for the forest elephant under climate change, particularly by guiding the designation of new protected areas to safeguard future climatically suitable habitats. Overall, this PhD research underscores the value of interdisciplinary approaches in climate change and ecological science. By integrating systematic literature reviews, local ecological knowledge and ecological modeling, this thesis provides a cross-scalar perspective on the direct and indirect impacts of climate change in the Congo Basin. Findings highlight, among others, the critical role of local ecological knowledge in environmental sciences, demonstrating how community-based observations can complement remote sensing and modeling efforts in data- deficient regions. From a policy perspective, this research provides valuable insights for biodiversity conservation strategies in the Congo Basin, particularly in light of global frameworks such as the Kunming-Montreal Global Biodiversity Framework and the Paris Agreement. Ultimately, this work contributes to a broader understanding of how climate change interacts with socio-ecological systems in one of the world’s most important tropical rainforests.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,128
Score d'incertitude au seuil0,255

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0040,004
Études des sciences et des technologies0,0010,001
Communication savante0,0040,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

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 Opus0,147
Tête enseignante GPT0,360
Écart entre enseignants0,213 · 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 source (Gemma direct ou Codex distillé), 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

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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