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Special Issue "Integrating Forest Health Patterns in Growth and Yield Models"

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Ecology and Management".

Deadline for manuscript submissions: 15 May 2019

Special Issue Editor

Guest Editor
Dr. Matthew B. Russell

Department of Forest Resources, University of Minnesota, 1530 Cleveland Ave. North, St. Paul, MN 55108, USA
Website | E-Mail
Interests: forest ecosystem health, forest disturbance, growth and yield, forest modeling, forest management

Special Issue Information

Dear Colleagues,

Disturbances and forest health threats influence the structure and composition of forest ecosystems and will likely increase in frequency and severity under future global change scenarios. In forest modeling scenarios, too often data from disturbed forests are overlooked in favor of quantifying the “average tree” and its growth potential. Disturbances that impact forest health, including insects, diseases, fire, herbivory, and weather damage, can have immediate impacts to forest growth and yield or can persist for long durations. The lack of information regarding the impacts and responses of forests emphasizes the need for quantifying the magnitude and uncertainties associated with forest health agents.

For this Special Issue of Forests, we invite papers that integrate the impacts of forest disturbance and forest health stressors in models that characterize forest growth and yield patterns. We welcome studies from diverse forest types that quantify growth and yield at the scale of the landscape, stand, and/or individual tree. Empirical, process-based, and/or hybrid model designs will be considered for this issue.

Dr. Matthew B. Russell
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • forest ecosystem health
  • forest disturbance
  • insects
  • disease
  • climate change
  • growth and yield
  • forest modeling
  • empirical models
  • process models
  • forest management

Published Papers (1 paper)

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Open AccessArticle Impacts of Alternative Harvesting and Natural Disturbance Scenarios on Forest Biomass in the Superior National Forest, USA
Forests 2018, 9(8), 491; https://doi.org/10.3390/f9080491
Received: 17 June 2018 / Revised: 1 August 2018 / Accepted: 10 August 2018 / Published: 12 August 2018
Cited by 2 | PDF Full-text (1912 KB) | HTML Full-text | XML Full-text | Supplementary Files
The amount of biomass stored in forest ecosystems is a result of past natural disturbances, forest management activities, and current structure and composition such as age class distributions. Although natural disturbances are projected to increase in their frequency and severity on a global [...] Read more.
The amount of biomass stored in forest ecosystems is a result of past natural disturbances, forest management activities, and current structure and composition such as age class distributions. Although natural disturbances are projected to increase in their frequency and severity on a global scale in the future, forest management and timber harvesting decisions continue to be made at local scales, e.g., the ownership or stand level. This study simulated potential changes in natural disturbance regimes and their interaction with timber harvest goals across the Superior National Forest (SNF) in northeastern Minnesota, USA. Forest biomass stocks and stock changes were simulated for 120 years under three natural disturbance and four harvest scenarios. A volume control approach was used to estimate biomass availability across the SNF and a smaller project area within the SNF (Jeanette Project Area; JPA). Results indicate that under current harvest rates and assuming disturbances were twice that of normal levels resulted in reductions of 2.62 to 10.38% of forest biomass across the four primary forest types in the SNF and JPA, respectively. Under this scenario, total biomass stocks remained consistent after 50 years at current and 50% disturbance rates, but biomass continued to decrease under a 200%-disturbance scenario through 120 years. In comparison, scenarios that assumed both harvest and disturbance were twice that of normal levels and resulted in reductions ranging from 14.18 to 29.85% of forest biomass. These results suggest that both natural disturbances and timber harvesting should be considered to understand their impacts to future forest structure and composition. The implications from simulations like these can provide managers with strategic approaches to determine the economic and ecological outcomes associated with timber harvesting and disturbances. Full article
(This article belongs to the Special Issue Integrating Forest Health Patterns in Growth and Yield Models)

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