WeChat Mini Program
Old Version Features

Spatial Patterns of Vigor by Stand Density Across Species Groups and Its Drivers in a Pre-Harvest Ponderosa Pine-Dominated Landscape in Northern California

FOREST ECOLOGY AND MANAGEMENT(2023)

Univ British Columbia

Cited 1|Views13
Abstract
Information on spatial patterns of forest health and stand vigor in historical forests is quite limited, particularly for forests of North America, which have been subsequently harvested. We used forest inventory data from 1934, which pre-dated early 20th century timber harvest, to reconstruct patterns of vigor in overstory trees. Across 4,000 ha of ponderosa pine-dominated forests in the Blacks Mountain Experimental Forest (BMEF), we described the spatial patterns of pre-harvest (1934) forests using a stand density index (SDI) and accounting for different species and vigor classes. We then identified topo-edaphic variables associated with pre-harvest SDI. To do so, we first calculated SDI for two species groups: 1) ponderosa pine (Pinus ponderosa Laws.) and Jeffrey pine (Pinus jeffreyi Grev. & Balf.), and 2) incense-cedar (Calocedrus decurrens (Torr.) Florin) and white fir (Abies concolor (Grod. and Glend.)) as well as for seven vigor classes within these two groups. Second, using Moran’s I, we found four spatially aggregated clusters (Moran’s I = 0.35): 1) ponderosa pine-high vigor, 2) ponderosa pine-low vigor, 3) mixed species-high vigor, and 4) mixed species-low vigor. Most of the pre-harvest landscape consisted of high vigor ponderosa pine clusters with low SDI (relative density (RD) < 0.25). High elevation sections consisted of mixed species clusters of both high and low vigor with high SDI (RD > 0.35). Using classification and regression tree analysis, we identified elevation (m), surface-soil depth (cm), and sub-soil depth (cm) were related to the clusters. A multinominal logistic regression model (kappa = 0.44; classification accuracy = 64.4 %) identified surface stoniness, available water capacity at 150 cm depth and its interaction with sub-soil depth as important variables that were related to clusters. Within ponderosa pine-dominated forests, managers consider dense stands of low vigor trees to be at-risk of insect, disease, and fire. Thus, our results can be used by managers as representative baselines for characterizing (pre-harvest) stand conditions to guide management treatments to support high vigor and low density forest conditions.
More
Translated text
Key words
Blacks Mountain Experimental Forest,Ponderosa pine,Spatial clusters,Topography
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined