CONSERVATION BIOLOGY BASED ON THE SPATIAL ANALYSIS
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".