CONSERVATION BIOLOGY BASED ON THE SPATIAL ANALYSIS
Notice bibliographique
Résumé
The use of spatial analysis in biology as a research tool has grown tremendously over the past decade and a half. Although biologists and ecologists have recognized the potential of spatial information for informing biology and policy for a long time, such as for studying changes and trends in populations and habitats, it has been only recently that spatial analysis has been incorporated into most biology studies. Since the 1990s, biology has developed quickly by the application of spatial analysis technologies. In this paper, we review the history, methodologies and applications of this tool, and the potential for growth and other applications by using some projects and works in which the authors were involved as examples. First, we discussed the use of spatially explicit data on biodiversity and its distribution, and the significance of using spatially explicit methods in biology was summarized. We presented patterns of biodiversity at the global scale and country level, and discussed plant diversity centers and vascular plant family diversity as monitored by the World Conservation Monitor Center (WCMC). We also discussed the spatial distribution of four groups (plant, birds, fishes and molluscs) of endangered species in the United States. Mapping the spatial distribution of biodiversity is a useful comparative tool for analyzing the patterns, magnitude and extent of biodiversity, changes in spatial distributions at different temporal scales, understanding the relationships between populations and habitats, and for by spatial overlap analysis as in GAP analysis. Second, we reviewed various projects including Global Forest Watch of World Resource Institute, National GAP Analysis of United States, Roadless Area of Forest Service-USA, and Nature Audit of Canada. Also, some examples from the literature were used, such as a comparative study of plant diversity richness between East Asia and North America and the spatial analysis of biological invasions. The spatial analysis of patterns of biodiversity and habitats were discussed in the third part of this paper. During the last two decades, pattern-oriented ecology and biology has made a lot progress, especially spatial pattern analyses, spatial statistics originating from geo-statistics, geographic information systems, spatially explicit model-based growth of individuals (grid), population theory based on patch analysis (e.g., metapopulations and source-sink models), and so on. The application of spatial pattern analysis in biology was summarized by examining two projects: the forest fragmentation analysis of the USA and late seral forests spatial pattern analysis in the Pacific Northwest, USA. We also presented the theory of Matrix conservation by Lindenmayer and Franklin, Conserving Forest Biodiversity, A Comprehensive Multiscaled Approach(2002). We agree with the authors of this new initiative that extends efforts beyond nature reserves to integrated strategies that balance and development at landscape or regional scales. Lastly, models that are used widely in biology, the spatially explicit model, process-based spatial model, agent-based spatial adaptation model (SWAM) and Dynamics Global Vegetation Model (DGVM), were discussed.This new branch of conservation, spatial biology, has matured as a new discipline that contains a lot of spatial and information technology and may make more contributions to the global biodiversity conservation.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».