Likelihood of occurrence of bark beetle attacks on conifer forests in Honduras under normal and climate change scenarios

Hernandez, Alexander J.; Saborío, Javier; Ramsey, R. Douglas; Rivera, Samuel

doi:10.1080/10106049.2011.650652

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…LR and LDA are commonly used linear classification MLAs. Hernandez et al [37] developed a spatial logistic regression model of the probability of bark beetle (Dendroctonus frontalis Zimmermann) attack in coniferous forests; the overall accuracy of the model was 68.7%. LR, which is a special case of the generalized linear model, models the log-odds as a linear function by minimizing the sum of the squared residuals.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Machine Learning Algorithms for Modeling the Spatial Distribution of Bark Beetle Infestation

Koreň

Jakuš

Zápotocký

et al. 2021

Forests

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Machine learning algorithms (MLAs) are used to solve complex non-linear and high-dimensional problems. The objective of this study was to identify the MLA that generates an accurate spatial distribution model of bark beetle (Ips typographus L.) infestation spots. We first evaluated the performance of 2 linear (logistic regression, linear discriminant analysis), 4 non-linear (quadratic discriminant analysis, k-nearest neighbors classifier, Gaussian naive Bayes, support vector classification), and 4 decision trees-based MLAs (decision tree classifier, random forest classifier, extra trees classifier, gradient boosting classifier) for the study area (the Horní Planá region, Czech Republic) for the period 2003–2012. Each MLA was trained and tested on all subsets of the 8 explanatory variables (distance to forest damage spots from previous year, distance to spruce forest edge, potential global solar radiation, normalized difference vegetation index, spruce forest age, percentage of spruce, volume of spruce wood per hectare, stocking). The mean phi coefficient of the model generated by extra trees classifier (ETC) MLA with five explanatory variables for the period was significantly greater than that of most forest damage models generated by the other MLAs. The mean true positive rate of the best ETC-based model was 80.4%, and the mean true negative rate was 80.0%. The spatio-temporal simulations of bark beetle-infested forests based on MLAs and GIS tools will facilitate the development and testing of novel forest management strategies for preventing forest damage in general and bark beetle outbreaks in particular.

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Section: Discussionmentioning

confidence: 99%

Assessment of Machine Learning Algorithms for Modeling the Spatial Distribution of Bark Beetle Infestation

Koreň

Jakuš

Zápotocký

et al. 2021

Forests

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“…This correlation does not necessarily indicate a direct effect of climate on the outbreaks, as an abundance of dense, unmanaged pine stands frequently weakened by wildfire could be the main driver. However, drought models for Honduras predict an increase in the overall susceptibility for SPB attacks in a climate change scenario [34]. Another climate-related factor that distinguishes Central America from other regions is the effect of low temperatures.…”

Section: Central Americamentioning

confidence: 99%

Towards Sustainable Forest Management in Central America: Review of Southern Pine Beetle (Dendroctonus frontalis Zimmermann) Outbreaks, Their Causes, and Solutions

Gomez

Sathyapala

Hulcr

2020

Forests

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Outbreaks of the southern pine beetle (SPB) Dendroctonus frontalis Zimmermann in Central America have had a devastating impact on pine forests. It remains unclear to what extent these outbreaks are caused by the beetle’s ecology, climate, and historical management practices. Using research data and experience accumulated in northern latitudes to guide management of forests in Central America is associated with great uncertainty, given the many unique features of the conifer forests in this region. The main recommendation from this review is that Central American bark beetle outbreaks need to be studied locally, and the local climate, biotic elements, and silvicultural history need to be considered. The key to reducing the impacts of SPB in Central America are local research and implementation of proactive management, in order to facilitate the establishment of forests more resilient to increasing environmental and anthropogenic pressures.

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Characterizing spatial patterns of pine bark beetle outbreaks during the dry and rainy season’s in Honduras with the aid of geographic information systems and remote sensing data

Valdez

Chen

Lin

et al. 2020

Forest Ecology and Management

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Likelihood of occurrence of bark beetle attacks on conifer forests in Honduras under normal and climate change scenarios

Cited by 4 publications

References 8 publications

Assessment of Machine Learning Algorithms for Modeling the Spatial Distribution of Bark Beetle Infestation

Assessment of Machine Learning Algorithms for Modeling the Spatial Distribution of Bark Beetle Infestation

Towards Sustainable Forest Management in Central America: Review of Southern Pine Beetle (Dendroctonus frontalis Zimmermann) Outbreaks, Their Causes, and Solutions

Characterizing spatial patterns of pine bark beetle outbreaks during the dry and rainy season’s in Honduras with the aid of geographic information systems and remote sensing data

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