Author Guidance

Thank you for your interest in contributing a chapter to the Updated Silvics of North America Project! This guide has been developed to provide approved chapter authors with guidance as they write their chapters. Please review this resource in full and reach out to one of the USNAP Coordinators if you have additional questions:

• Emma Gates, USDA Forest Service, emma.gates@usda.gov
• Sébastien Meunier, Canadian Forest Service, sebastien.meunier@nrcan-rncan.gc.ca 

Chapter Phase Overview

Any chapter included in the Silvics of North America will go through the following phases: 1) Chapter Development, 2) Chapter Submission, 3) Peer Review, 4) Chapter Acceptance, and 5) Chapter Maintenance. Please note all submitted chapters must pass the peer review in order to be added to the Silvics of North America. A chapter submission does not guarantee chapter acceptance. An overview of these phases can be seen in the table below:

Scholastica Access

The USNAP team uses Scholastica. Scholastica is a modern academic publishing platform that streamlines the traditional, manuscript review process and provides an efficient online system for submissions, peer review, and publication.

 

Please view the Chapter Outline in your preferred language below:

English Español Français

The Silvics of North America will be published as a digital interactive web product. As such, the raw data from your submission will be extracted and reformatted to comply with publication requirements and optimal display across media devices. However, all authors must submit their drafts in accordance with the formatting guidelines below to ensure the effective extraction of information and uniformity in the review process.

TEXT – General Guidance

All newly authored chapters for the Updated Silvics of North America Project must follow the USNAP chapter outline. This outline is available for viewing on the USNAP’s website, and should have been included in your onboarding package. Within the outline you will find the following levels of detail: 1) Chapter Sections, 2) Headings, 3) Subheadings, and 4) Author Guidance Statements. Sections, headings, and subheadings are all intended for inclusion. Further delineation in Author Guidance Statements in the chapter outline represent topical content suggestions, not formatting requirements. Do not use headings beyond the subheading level. Content within headings or subheadings should instead be categorized simply by paragraph separation. Minor deviations from the outline will be allowed as explained in the Lead Author Application form. For your draft, please follow the outline plan that you submitted in your application.

To begin drafting, please download the “Fillable Template” form which was included in your onboarding package. Although this is a fully editable document, please only add text where it is indicated. If you need to delete or add sections/headings/subheadings to align with the outline plan you submitted in your application, please copy and maintain the style represented throughout the document.

TEXT – Font

All chapters must be submitted in English, the official international language of science. Please maintain the “USNAP Text Formatting” style selections in the Fillable Template for all written material.

Body text should be Arial, Size 12, Single Spaced. Align the text with the appropriate heading (left-aligned) or subheading (indented by 1 tab = 0.4”). Indent the first line of all new paragraphs; the default tab is preset to 0.4. Please put only one space after the period at the end of a sentence.

TEXT – Units

Please adhere to the following:

• As part of this update, the Silvics of North America will use the standard international scientific measurement system, which is metric.
• Display all units in metric first, followed by the U.S. historical equivalent in parentheses. Metric units can be abbreviated, but please spell out English units.
• For figures, units should be abbreviated if they have been introduced earlier in the text.
• Example: 2,320 m³ ha^-1 (33,150 feet³ acre^-1)
• Spell out “percent” in the text and, space permitting, in tables.

TEXT – Citations

References appearing in the text are listed alphabetically at the end of the chapter. Cite resources in text alphabetically by the author-date system:

• (Smith, 2006; Smith et al., 2008; Trevor, 2004a; Trevor, 2004b)
• Note that a comma precedes the publication date. Each entry is separated by a semicolon.
• In Literature Cited, please provide DOI or other currently functioning stable link if available. Books, chapters, and individual papers in a proceedings do not need a DOI.

Examples of Literature Cited style:

1. Article in journal

Keng, S.-H.; Lin, C.-H.; Orazem, P.F. 2017. Expanding college access in Taiwan, 1978–2014: effects on graduate quality and income inequality. Journal of Human Capital. 11(1): 1–34. https://doi.org/10.1086/690235.

2. Book

Grazer, B.; Fishman, C. 2015. A curious mind: the secret to a bigger life. New York, NY: Simon and Schuster. 318 p.

3. Chapter in book

Thoreau, H.D. 2016. Walking. In: D’Agata, J., ed. The making of the American essay. Minneapolis, MN: Graywolf Press: 167–195.

4. Proceedings

Blake, J.I.; Somers, G.L.; Ruark, G.A. 1990. Perspectives on process modeling of forest growth responses to environmental stress. In: Dixon, R.K., ed. Proceedings, process modeling of forest growth responses to environmental stress conference. Portland, OR: Timber Press: 9–20.

5. Publication with more than 6 authors

Banzhaf, G.M.; Matney, T.G.; Schultz, E.B.; Meadows, J.S.; Jeffreys, J.P.; Booth, W.C.; [et al.]. 2016. Log-grade volume distribution prediction models for tree species in red oak-sweetgum stands on US mid-south minor stream bottoms. Forest Science. 62(6): 671–678. https://doi.org/10.5849/forsci.15-138. List only the first six authors, followed by [et al.].

6. Publication with 6 authors

Hussain, A.; Munn, I.A.; Grado, S.C.; West, B.C.; Jones, W.D.; Jones, J. 2007. Hedonic analysis of hunting lease revenue and landowner willingness to provide fee-access hunting. Forest Science. 53(4): 493–506. https://doi.org/10.1093/forestscience/53.4.493. List all six authors.

7. Technical report

Fischer, W.C.; Bradley, A.F. 1987. Forest ecology of western Montana forest habitat types. Gen. Tech. Rep. GTR-INT-223. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 95 p. https://doi.org/10.2737/INT-GTR-223.

8. Thesis/dissertation

Lee, J.M. 2020. Family forest owners satisfaction with timber transactions. Graduate Theses, Dissertations, and Problem Reports. 7689. Morgantown, WV: West Virginia University. 61 p. MS thesis. Available at: https://researchrepository.wvu.edu/etd/7689. (9 August 2023). Does the link require one more “click” to take the reader to the publication? If so, include “Available at:”.

8. Web publication

Arbor Day Foundation. 2020. Sawtooth oak: Quercus acutissima. Lincoln City, NE. https://www.arborday.org/trees/treeGuide/TreeDetail.cfm?ItemID=880. (2 April 2020). Note: Publication year is “last updated” or “last modified” date for the webpage. If website hadn’t provided that detail, citation would be (Arbor Day Foundation, n.d.) and, in Literature Cited, Arbor Day Foundation. [N.d.]. Date in parentheses is most recent date of access by author.

TEXT – Word Count Limits

The SNA is primarily a field guide to tree species management and has never been meant to be an exhaustive literature review of all available species science. To ensure the Silvics of North America delivers science in a readable format, the USNAP will be prescribing word count limits for overall chapters, as well as chapter sections. These limits represent size maximums, although shorter chapters are encouraged. For sections where you believe a size extension should be granted, please clarify this request when you submit your first draft. Extension requests will be considered during the chapter review process. Any supplemental content that is too detailed for the main chapter, or too extensive, may be added to the chapter appendices.

Word count limits are specified in table 3.1 below. As indicated in table 3.1, a larger allowance is given for species with the greatest volume of related published content. The list of the 175 species with the most published content can be seen in table 3.2

TEXT – Use of 1990 SNA Material

One of the first steps in producing your chapter will be to review the current literature about your species to determine what advancements in understanding have been made since the 1990 SNA publication. The research has progressed significantly since 1990 (e.g., genetics, current and future species range) while other species-specific information may be the same (e.g., tree morphology).  If you use content from the 1990 SNA, please find and cite the original source of that information where possible, and verify that all your chapter content reflects the most current research.

However, after you conduct your literature review you may find that some passages in the 1990 SNA: A) are still the most concise and accurate way to describe a topic, and B) fit within the new chapter outline structure.  If this is true and you believe it is needed to use certain passages from the 1990 SNA verbatim, please follow the steps below:

1. If contact information is available in table 3.3 below, please contact the 1990 author and request permission to use their statements.
   1. If you receive a reply from the 1990 author, and they give permission to use their material, the 1990 text can be used and the 1990 author should be added as a co-author.
   2. If you receive a reply from the 1990 author, and they decline permission, then the 1990 text cannot be used.  In this case authors should rewrite the intended material into their own language.
   3. If you are unable to receive a reply, the 1990 text can be used and the 1990 authorship team should be included in the acknowledgments section along with a clarifying statement indicating that some material came directly from the 1990 publication. The 1990 publication should still be cited, and depending on the amount of content, the previous authors should be added as co-authors.
2. If contact information is not available in table 3.3 below, the 1990 text can be used and the 1990 authorship team should be included in the acknowledgments section along with a clarifying statement indicating that some material came directly from the 1990 publication. The 1990 publication should still be cited, and depending on the amount of content, the previous authors should be added as co-authors.

Due to the amount of time since publication, many of the authors involved in the 1990 SNA are no longer available.  The list below shows all 1990 SNA chapter authors for which contact information was publicly available.

FIGURES and TABLES – General Guidance

As labeled in the chapter outline, the following figures and data will be provided for authors by the USNAP’s Distribution and Environmental Associations Team for species where the data are available. Authors should reference these in the text and include captions and alternate text like any other figure.

• Figure 2.1 (e.g., US-Only_Actual_sp838.png) OR (e.g., US-Only_CurrentPredicted_Consensus_sp838.png)
• Figure 2.2a (e.g., US-Only_HQCL_Consensus_sp838.png)
• Figure 2.2b (e.g., US-Only_HQCL_Consensus_sp838.png)
• Data for table 2.1 (e.g., mat_USonly_stats_sp838.csv, map_USonly_stats_sp838.csv, elv_USonly_stats_sp838.csv)

To access the resources indicated above, open the Box folder “01_Figures and Tables”. Access instructions are provided under Box / Pinyon Access in section “2. Key Information” of this document. Once you have opened 01_Figures and Tables, navigate to the established species folder for your species, and click on the title to open (e.g., “Quercus virginiana (FT)”). To sort species folders alphabetically, simply click “Name” in the upper left corner of the screen. Once you have opened your species folder, you will see a display similar to what is shown on the following page:

Figure 3.1–Species folder within 01_Figures and Tables

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To download any of the USNAP produced files, simply click on the file name, then click download in the upper right corner.

Figure 3.2–Map file available for download

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Optional figures and table that authors may choose to source independently and include are:

• Up to 5 figures that can be placed where the author deems appropriate
• Table 7.1
• Figure 7.1

To achieve uniformity, the USNAP is prioritizing limiting figures and tables to the above. However, if there are other figures or tables you feel are critical to your chapter, please include them and indicate your reasoning for doing so in your 1^st draft submission. Do not repeat material from a table in a figure and vice versa. Choose the best format to display the information once. Please limit the numbers of panels in a figure. All figures and tables must be self-produced or in the public domain, or comply with copyright standards and be cleared for use. USNAP provided material (Figures 2.1, 2.2a, 2.2b, and data for table 2.1) is considered cleared for use by authors. The USNAP will credit the relevant contributors in the publication.

Please submit figures and tables as individual files for your chapter submission. You may choose to include any figures or tables in the body of your word document as well.  Please include all relevant captions in the body of your chapter submission where the relevant figure or table should be placed. Remember to provide alternative text for figures.

If you have questions about Figures 2.1, 2.2a, 2.2b, or data for table 2.1, please refer to Appendix A or contact the following:

• Coniferous and Deciduous Species
  • Anantha Prasad, USDA Forest Service, anantha.prasad@usda.gov
• Matt Peters, USDA Forest Service, matthew.p.peters@usda.gov
  • John Pedlar, Canadian Forest Service, john.pedlar@NRCan-RNCan.gc.ca
  • Dan McKenney, Canadian Forest Service, dan.mckenney@NRCan-RNCan.gc.ca
  • Lauro López Mata, Colegio de Postgraduados, lauro@colpos.mx
• Exotic and Invasive Species
• Jacob Fraser, USDA Forest Service, Jacob.Fraser@usda.gov

FIGURES and TABLES – Figure Formatting

Overall design (including type, line weight, and other visual keys) should be consistent across all figures; for example, if a solid line represents timber volume in one graph, then use the same solid line for timber volume in all other figures. When creating line graphs, if using color, also use simple symbols for data points as well as patterns to differentiate the lines of information. Use three-dimensional graphs only when data need to be presented on three axes to be meaningful. Three-dimensional pie charts misrepresent data. Do not use 3-D in graphs having only X and Y axes.

If including numerical data, provide units in metric. Axis labels should be descriptive (not just units) and show units in parentheses. Capitalize only the first letter of the first word in an axis label (use sentence case) except for proper names.

Example: “Area harvested (ha)

Photos should be in .jpg or .png format, at least 72 ppi (sometimes called dpi), and in RGB color format. Other graphics such as maps should be in .gif or .png format. Original artwork should not contain the artist’s signature or watermark.

Figure material should not be duplicated in tables or vice versa. Figure data should “stand alone” with sufficient sourcing and explanation to be fully understood. Submit separate digital files for each figure in your document. Acceptable file formats include: .xslx, .ai, .jpg, .gif, or .png.

FIGURES and TABLES – Table Formatting

Table headings should be in sentence case (only first words and proper nouns are capitalized). Titles should fully identify the what, where, and when of the data in the table. The title is not necessarily a complete sentence and does not end with a period. Additional explanations are put in table footnotes, not added to the title. The word “table” is not capitalized in the manuscript text unless it begins a sentence. Units of measure are centered above the data columns they pertain to and do not belong in the table headings. Units are spelled out only if they have not yet been introduced in the text. The % symbol is used only when space is tight. Indent “Total” or “Average” (always singular) from left margin. A total line is placed under the columns being totaled. If there are both subtotals and a grand total, the subtotals are indented more than the heading they go with, and the grand total is flush left. An extra line space is left before the grand total.

Table material should not be duplicated in figures or vice versa. Table data should “stand alone” with sufficient sourcing and explanation to be fully understood. A single column of data or a simple tabulation of two columns is included in the text, not treated as a table, and is called a “tabulation” when referred to in the text. Provide each table as a separate Word file. You may simply copy and paste the example tables below and edit as necessary. If including numerical data, provide units in metric.

Please reference tables 3.4 and 3.5 on the following page as an example of appropriate table formatting. Table 3.4 shows the appropriate formatting for table 2.1 from the Chapter Outline. Table 3.5 shows the appropriate formatting for table 7.1 from the Chapter Outline. Blank copies of these tables have been included for use in your onboarding package.

Table 3.4—Temperature, precipitation, and elevation for range for live oak (labeled as table 2.1 in the Chapter Outline)

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Table 3.5—Significant insects and diseases impacting live oak (Labeled as Table 7.1 in the Chapter Outline)

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FIGURES and TABLES – Captions (Titles)

Include all captions for your figures or tables in the body of your chapter when you submit your draft. Include captions in the text where the appropriate figure or table would be placed. Figures and tables should be numbered consecutively regardless of the section they are intended for. Numbering in appendices starts with “A” (e.g., the fourth figure in the appendix would be A.4). Tables should be numbered independently from figures. Identify the figure completely, including the definitions of any acronyms within it, so that it stands alone. Ideally, captions should answer the who, what, when, and where of the graphic. The figure number should be followed by a dash (—), e.g., “Figure 4—Forest land area by ownership.” Each photo used requires a credit, and images must be in the public domain or the contributor must have the right to reuse the image. Include any title, the artist/photographer, original publication year, and where the image currently resides.

For tables requiring explanatory notes, numbered footnotes, and source notes, please place this information directly under the caption in the text. Explanatory notes come before any lettered footnotes; for example: Note: data may not add to totals or agree exactly with other tables because of rounding. — = unknown. N.d. = no data. Table footnotes are lowercase, italic letters (“a” is the first footnote in every table). The letters are placed at the end of a heading or other piece of data. The letters are in alphabetical order from left to right, boxhead by boxhead and then line by line in the field. Source notes come after any lettered footnotes, are in author-date format, and end with a period. For example: Source: Bailey (1995).

Figure caption examples:

• Figure 1—Mean temperature and relative humidity by sample period.
• Figure 2—Uncut forest at Hoodoo in the Blue Mountains of northeastern Oregon, USA, 1955: (A) protected, and (B) grazed. Courtesy photos by Thomas Waters, Oregon Department of Forestry.
• Figure 3—Juvenile steelhead trout. USDA Forest Service photo by Pacific Northwest Region.
• Figure 4—Celilo Falls on the Columbia River, 1947. Courtesy illustration by Traci Merritt, Alsea Images, LLC.

FIGURES and TABLES – Alternate Text for Figures

In addition to captions, please provide alternate text for all figures in the text of your chapter below the caption. This will enable screen reading software to describe the graphic to visually impaired users. For photographs, the alternate text should give a general description of the image; for graphs and charts, it should describe the overall meaning of the graphic without detailing each data point. Please see examples below for the difference between Captions and Alternate Text:

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Caption: Figure 1—Forester Anna Jones shows Student Conservation Association volunteers how to plant seedlings at Olympic National Park, June 2016. USDA Forest Service photo by Frank Vanni.

Alternate Text: Photo of a forester demonstrating planting techniques to volunteers gathered around her.

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Caption: Figure 2—Total costs for Seattle, Washington, USA, parks restoration events held between January 1, 2011, and June 30, 2013.

Alternate Text: Chart depicting cost comparison across categories. Labor is the highest, followed by tools, staff, plants, preparation, and materials.

For those of you writing about exotic and invasive species, you may be finding only limited information on such topics as species management/control and susceptibility/response to disturbances in North America. To supplement this knowledge, you may be looking at documentation in the species’ original habitat and wondering about its relevance. How applicable to the Silvics of North America are reported results from the species’ native range in Asia, Australia, Europe, or elsewhere beyond this continent? With an eye on word count limits, you may be asking, “How do we decide whether to include information from outside North America?”

The following guidance from USNAP reviewers and other team members is intended to help you answer these questions. First, include as much sound information as possible from studies in North America. Second, draw upon research from the species’ native range, acknowledging that results may be only a proxy for the suite of conditions in North America. Pay attention to the sideboards--for example, soils and topography may be similar in the location of origin and the current or projected range in North America, but the climate may differ. Climate in the native and nonnative ranges may be similar, but native insects and disease or competitors keep the species in check in its native setting. In reporting information from these alternative locations, consider how “results may vary” for the species because of differences in edaphic, climatic, biotic, and other factors. But refer to both global and local management practices and disturbance considerations in your coverage.

Last, in learning that few studies document local management and disturbance responses, you have the opportunity to highlight the need for more research. List these knowledge needs in section 13, Knowledge Gaps. Further research on the topics you identify will help expand understanding of nonnative species and prepare managers and others to confront the potentially rapid adaptation and expansion/migration of your species.

 

The first draft of your species chapter is due within 1 year of being accepted as a Lead Author and receiving your onboarding package. All chapter drafts must be submitted in English. As a quick summary, please upload your chapter draft to Scholastica using this link: https://silvicsofnorthamerica.scholasticahq.com/for-authors. Then notify the project team. Please see full instructions below.

The USNAP peer review process will involve the following:

1. The chapter will go through an editorial review by the Managing Editor. The section co-leads will be notified of the chapter’s submission, and will decide which of them will review the chapter sections in Step 4.
2. The Project Lead will do a pre-review.
3. The Managing Editor and Coordinators will evaluate any reviewer suggestions and search for reviewers based on expertise and publication history. They will reach out to reviewers until two have accepted and ask them to complete the review in 30 days.
   • If no one agrees or responds, and the author hasn’t already provided reviewers, the coordinators will ask the author for suggestions.
   • The coordinators can search for other options.
   • The final option will be asking two of the section co-leads to do a complete review.
4. After the two reviews are complete, the Managing Editor will send the chapter to section co-leads for a check of the review and a rating determination (major or minor revisions, etc.). Co-leads may add their own comments at this time, but it is not necessary. Co-leads may also delegate this role to a team member.
5. The chapter will be returned to the author with an overall rating. The author will have 1 month to submit a second draft.
   • If any sections require major revisions, that will be the overall grade.
   • If any sections require minor revisions, that will be the overall grade.
6. Based on section ratings, the revised chapter will
   • Go back to co-leads to review sections with major revisions (co-leads can choose to consult reviewers or not) and/or
   • Go to the Project Lead to check minor revisions and approve or send back.
7. The chapter will be returned to the author within 1 month to submit a final draft.
8. The chapter will go to a species group reviewer to ensure all comments have been appropriately addressed. The species group reviewer will decide whether to accept or reject the chapter.
9. The Project Lead will conduct a final review for policy.
10. The Managing Editor will complete technical editing.

11. The finished chapter will be sent to the U.S. Forest Service’s Office of Communication for approval.

     

If a chapter is rejected at any point in the process, the Managing Editor will contact you about available options which may include resubmitting your chapter. If any of the above timelines change, you will be notified by the Managing Editor.

Once a chapter has passed the USNAP’s peer review it will undergo the steps listed in table 6.1 below. Note that the steps and deadlines are approximate; Cynthia Moser and Susan Iott will work with you individually to ensure the necessary steps are taken. You will be asked to validate the Proof Copy after your chapter’s acceptance, but prior to publishing. All chapters will be released as individually citable publications with credit given to all authors and associated reviewers at the beginning of each chapter. All published chapters will undergo translation into Spanish and French.

Many of the chapters published in the 1990 Silvics of North America were written during the 1980s. This has resulted in a manual that does not capture scientific findings from the last 30 to 40 years. A key goal of the USNAP has been to address this research gap and ensure that once a chapter is published as part of the USNAP it will remain current.

Because of this, the USNAP will be introducing a maintenance schedule that all chapters will be entered into once they are published. This schedule will be managed by the USNAP’s Maintenance Committee. Once a chapter is entered into the schedule, it will be reassessed for future updates according to the timeline deemed appropriate by the committee. Lead Authors for any published chapters will be contacted first to assess and update their chapter with any new relevant research. If no one from the original authorship team is available or interested, the opportunity to assist in the chapter update will be listed publicly.

The following resources have been centralized to assist in locating tree species information. Note: climate change and other factors have significantly changed the structure and function of many tree species. Use caution in referencing information published before 2000. Conduct a literature review to verify that older reference information is still correct before citing.

General Resources

• 1990 Silvics of North America
  • Chapters in USNAP format: https://research.fs.usda.gov/silvics/species-chapters/
  • Primary publication: https://www.srs.fs.usda.gov/pubs/misc/ag_654/table_of_contents.htm
• Checklist of insects and mites (1990 SNA)
  • https://www.srs.fs.usda.gov/pubs/misc/ag_654/volume_1/checklist_of/insects_and_mites.htm
• Checklist of Organisms Causing Tree Diseases (1990 SNA)
  • https://www.srs.fs.usda.gov/pubs/misc/ag_654/volume_1/checklist_of/organisms_causing_tree_diseases.htm
• Checklist of Birds (1990 SNA)
  • https://www.srs.fs.usda.gov/pubs/misc/ag_654/volume_1/checklist_of/birds.htm
• Checklist of Mammals (1990 SNA)
  • https://www.srs.fs.usda.gov/pubs/misc/ag_654/volume_1/checklist_of/mammals.htm
• Tree-size Class
  • FIA Glossary: Standard Terminology, Revision 10.27.2022 (usda.gov)
  • When differentiating between seedling, sapling, poletimber, and sawtimber class sizes please use UDSA Forest Service FIA standards as a baseline.  When needed you may reference other species-specific literature.  In all cases, please provide the numerical values in your chapter that you are using to delineate between class sizes.

Section 2. Distribution and Environmental Associations

• Current and Future Tree Distributions
  • Beaudoin, A.; Bernier, P.Y.; Guindon, L.; Villemaire, P.; Guo, X.J.; [et al.]. 2014. Mapping attributes of Canada’s forests at moderate resolution through kNN and MODIS imagery. Canadian Journal of Forest Research. 44(5): 521–532. https://doi.org/10.1139/cjfr-2013-0401.
  • Hermosilla, T.; Bastyr, A.; Coops, N.C.; White, J.C.; Wulder, M.A. 2022. Mapping the presence and distribution of tree species in Canada’s forested ecosystems. Remote Sensing of Environment. 282: 113276. https://doi.org/10.1016/j.rse.2022.113276.
  • Iverson, L.R.; Prasad, A.M., Matthews, S.N.; Peters, M. 2008. Estimating potential habitat for 134 eastern US tree species under six climate scenarios. Forest Ecology and Management. 254(3): 390–406. https://doi.org/10.1016/j.foreco.2007.07.023.
  • McKenney, D.W. 2022. Plant hardiness zones of Canada. Natural Resources Canada. http://planthardiness.gc.ca/.
  • McKenney, D.W.; Pedlar, J.H.; Lawrence, K.; Campbell, K.; Hutchinson, M.F. 2007. Potential impacts of climate change on the distribution of North American trees. BioScience. 57(11): 939–948. https://doi.org/10.1641/b571106.
  • McKenney, D.W.; Pedlar, J.H.; Rood, R.B.; Price, D. 2011. Revisiting projected shifts in the climate envelopes of North American trees using updated general circulation models. Global Change Biology. 17(8): 2720–2730. https://doi.org/10.1111/j.1365-2486.2011.02413.x.
  • Peters, M.P.; Iverson, L.R.; Prasad, A.M.; Matthews, S.N. 2019. Utilizing the density of inventory samples to define a hybrid lattice for species distribution models: DISTRIB‐II for 135 eastern U.S. trees. Ecology and Evolution. https://doi.org/10.1002/ece3.5445.
  • Prasad, A.; Pedlar, J.; Peters, M.; McKenney, D.; Iverson, L.; Matthews, S.; Adams, B. 2020. Combining US and Canadian forest inventories to assess habitat suitability and migration potential of 25 tree species under climate change. Diversity and Distributions. https://doi.org/10.1111/ddi.13078.
  • Prasad, A.M. 2018. Machine learning for macroscale ecological niche modeling—a multi-model, multi-response ensemble technique for tree species management under climate change. In: Humphries, G.R.; Magness, D.R.; Huettmann, F., eds. Machine learning for ecology and sustainable natural resource management. Springer International Publishing. https://doi.org/10.1007/978-3-319-96978-7.
  • Schwartz, M.W.; Iverson, L.R.; Prasad, A.M. 2001. Predicting the potential future distribution of four tree species in Ohio using current habitat availability and climatic forcing. Ecosystems. 4(6): 568–581. https://doi.org/10.1007/s10021-001-0030-3.
  • Wilson, B.T.; Lister, A.J.; Riemann, R.I. 2012. A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. Forest Ecology and Management. 271: 182–198. https://doi.org/10.1016/j.foreco.2012.02.002.
  • Wilson, B.T.; Lister, A.J.; Riemann, R.I.; Griffith, D.M. 2013. Live tree species basal area of the contiguous United States (2000–2009). https://www.fs.usda.gov/rds/archive/catalog/RDS-2013-0013.
  • Wilson, B.T.; Knight, J.F.; McRoberts, R.E. 2018. Harmonic regression of Landsat time series for modeling attributes from national forest inventory data. ISPRS Journal of Photogrammetry and Remote Sensing. 137: 29–46. https://doi.org/10.1016/j.isprsjprs.2018.01.006.
• Tree Migration Rates, Barriers, Refugia, etc.
• Boisvert‐Marsh, L.; Pedlar, J.H.; de Blois, S. ; Le Squin, A. ; Lawrence, K.; [et al.]. 2022. Migration‐based simulations for Canadian trees show limited tracking of suitable climate under climate change. Diversity and Distributions. 28(11): 2330–2348. https://doi.org/10.1111/ddi.13630.
  • Boisvert-Marsh, L.; Périé, C.; de Blois, S. 2014. Shifting with climate? Evidence for recent changes in tree species distribution at high latitudes. Ecosphere. 5(7): 1–33. https://doi.org/10.1890/es14-00111.1.
  • Fei, S.; Desprez, J.M.; Potter, K.M.; Jo, I.; Knott, J.A.; Oswalt, C.M. 2017. Divergence of species responses to climate change. Science Advances. 3(5): e1603055. https://doi.org/10.1126/sciadv.1603055.
  • McLachlan, J.S.; Clark, J.S.; Manos, P.S. 2005. Molecular indicators of tree migration capacity under rapid climate change. Ecology. 86(8): 2088–2098. https://doi.org/10.1890/04-1036.
  • McLachlan, J.S.; Clark, J.S. 2004. Reconstructing historical ranges with fossil data at continental scales. Forest Ecology and Management. 197(1–3): 139–147. https://doi.org/10.1016/j.foreco.2004.05.026.
  • Miller, K.M.; McGill, B.J. 2018. Land use and life history limit migration capacity of eastern tree species. Global Ecology and Biogeography. 27(1): 57–67. https://doi.org/10.1111/geb.12671.
  • Prasad, A.M.; Gardiner, J.D.; Iverson, L.R.; Matthews, S.N.; Peters, M. 2013. Exploring tree species colonization potentials using a spatially explicit simulation model: implications for four oaks under climate change. Global Change Biology. 19(7): 2196–2208. https://doi.org/10.1111/gcb.12204.
  • Schwartz, M.W. 1992. Modelling effects of habitat fragmentation on the ability of trees to respond to climatic warming. Biodiversity and Conservation. 2(1): 51–61. https://doi.org/10.1007/BF00055102.
  • Sittaro, F.; Paquette, A.; Messier, C.; Nock, C.A. 2017. Tree range expansion in eastern North America fails to keep pace with climate warming at northern range limits. Global Change Biology. 23(8): 3292–3301. https://doi.org/10.1111/gcb.13622.
  • Stralberg, D.; Carroll, C.; Pedlar, J.H.; Wilsey, C.B.; McKenney, D.W.; Nielsen, S.E. 2018. Macrorefugia for North American trees and songbirds: climatic limiting factors and multi‐scale topographic influences. Global Ecology and Biogeography. 27(6): 690–703. https://doi.org/10.1111/geb.12731.
  • Zhu, K.; Woodall, C.W.; Clark, J.S. 2012. Failure to migrate: lack of tree range expansion in response to climate change. Global Change Biology. 18(3): 1042–1052. https://doi.org/10.1111/j.1365-2486.2011.02571.x.
• Environmental Drivers of Tree Distributions
  • Ettinger, A.K.; Ford, K.R.; HilleRisLambers, J.2011. Climate determines upper, but not lower, altitudinal range limits of Pacific Northwest conifers. Ecology. 92(6): 1323–1331. https://doi.org/10.1890/10-1639.1.
  • Michaelian, M.; Hogg, E.H.; Hall, R.J.; Arsenault, E. 2011. Massive mortality of aspen following severe drought along the southern edge of the Canadian boreal forest. Global Change Biology. 17(6): 2084–2094. https://doi.org/10.1111/j.1365-2486.2010.02357.x.
  • Peltier, D.M.; Barber, J.J.; Ogle, K. 2018. Quantifying antecedent climatic drivers of tree growth in the Southwestern US. Journal of Ecology. 106(2): 613–624. https://doi.org/10.1111/1365-2745.12878.
  • Savage, J.; Vellend, M. 2015. Elevational shifts, biotic homogenization and time lags in vegetation change during 40 years of climate warming. Ecography. 38(6): 546–555. https://doi.org/10.1111/ecog.01131.

Section 3. Life History Traits, Reproduction, and Early Growth

• This section is based on the “Life History” of the previous Silvics of North America (Burns and Honkala, 1990).
• Sexual reproduction, asexual reproduction, and respective growth rates are described.
• Terminology related to sexual reproduction:
  • Seed production, high-forest method, seedling, scarification, tolerances
• Terminology related to asexual reproduction:
  • Vegetative reproduction; low-forest method, layering
  • Distinguish between epicormic and true vegetative regeneration

Section 4. Tree Growth and Stand Dynamics

• This section is also based on “Life History” (Burns and Honkala, 1990).
• Focus on identifying plant associations, site conditions, successional stages, structural development, and growth rates and yields within and across various developmental stages. Cover sapling to biological maturity.
• Related Terminology:
  • Light tolerance, plant associations, habitat types, site index, SAF cover types

Section 5. Management

• Focus on how management can assist, promote, hinder, or alter regeneration pathways. Topics covered are silvicultural systems; silvicultural options; managing with natural and planted regeneration; and intermediate tending and management.
• Indicate what approaches have traditionally been used; what has attempted and failed; how approaches have evolved. Not every aspect needs to be addressed.
• Helpful statements could include:
  • “Successful regeneration methods have included…”
  • “Propagation techniques that have and have not worked…”
  • “Things to consider no matter the objective”
• Not covered within this section are specific strategies, tactics, and treatments that rely upon site-specific objectives and require a more thorough silviculture or manager’s handbook.
• Terminology related to natural regeneration:
  • Systems, age, structure options, regional considerations, site preparation, advance regeneration
• Terminology related to planted regeneration:
  • Planted > artificial terminology; direct seeding

Section 6. Genetics

Seed Transfer Guidance

• A document detailing seed transfer guidelines was published by USDA in May 2024. These guidelines are for a collection of important tree species in the eastern United States and do not include information about the seed-transfer of every species being assessed in the USNAP. Seed-Transfer Guidelines for Important Tree Species in the Eastern United States (usda.gov)
• Even if this document does not address a given species, it may be used as a reference for the type of information that is expected to be in this section.

Section 8. Disturbance Regime: Wildland Fire

Sections should reflect main considerations of how fire affects the tree species, citing key references but without exhaustive detail. Information for each subsection will not exist for all species. Include knowledge gaps and uncertainty as appropriate for each section.

• Selected Readings: Probability of mortality from fire
  • Cansler, C.A.; Hood, S.M.; van Mantgem, P.J.; Varner, J.M. 2020. A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species. Fire Ecology. 16: 25. https://doi.org/10.1186/s42408-020-00082-0. *See supplemental information for specifics on individual species
  • Hood, S.; Lutes, D. 2017. Predicting post-fire tree mortality for 12 western US conifers using the First-Order Fire Effects Model (FOFEM). Fire Ecology. 13: 66–84. https://doi.org/10.4996/fireecology.130290243.
  • Hood, S.; Varner, M.; van Mantgem, P.; Cansler, C.A. 2018. Fire and tree death: understanding and improving modeling of fire-induced tree mortality. Environmental Research Letters. 13: 113004. https://doi.org/10.1088/1748-9326/aae934. *Provides general overview of subject with many references in the literature cited for specific species
  • Keyser, T.L.; McDaniel, V.L.; Klein, R.N.; Drees, D.G.; Burton, J.A.; Forder, M.M. 2018. Short-term stem mortality of 10 deciduous broadleaved species following prescribed burning in upland forests of the Southern US. International Journal of Wildland Fire. 27: 42–51. https://doi.org/10.1071/wf17058.
• Selected Readings: Tree resistance and species responses to fire
  • Hood, S.M.; Harvey, B.J.; Fornwalt, P.J.; Naficy, C.E.; Hansen, W.D.; [et al.]. 2021. Fire ecology of Rocky Mountain forests. In: Collins, B.; Greenberg, C.H., eds. Fire ecology and management: past, present, and future of US forested ecosystems. SpringerNature. *US Rocky Mountain species – see tables 8.1 and 8.2
  • Rodríguez-Trejo, D.A.; Fulé, P.Z. 2003. Fire ecology of Mexican pines and a fire management proposal. International Journal of Wildland Fire. 12: 23–37. https://doi.org/10.1071/wf02040. *Mexican pines
  • Stevens, J.T.; Kling, M.M.; Schwilk, D.W.; Varner, J.M.; Kane, J.M. 2020. Biogeography of fire regimes in western U.S. conifer forests: a trait-based approach. Global Ecology and Biogeography. https://doi.org/10.1111/geb.13079. *Western U.S. species
• Selected Readings: Second-order effects and interactions with fire
  • Kane, J.M.; Varner, J.M.; Metz, M.R.; van Mantgem, P.J. 2017. Characterizing interactions between fire and other disturbances and their impacts on tree mortality in western U.S. forests. Forest Ecology and Management. 405: 188–199. https://doi.org/10.1016/j.foreco.2017.09.037.
  • Fettig, C.J.; Runyon, J.B.; Homicz, C.S.; James, P.; Ulyshen, M.D. 2022. Fire and insect interactions in North American forests. Current Forestry Reports. 8(4): 301–316. https://doi.org/10.1007/s40725-022-00170-1.
  • van Mantgem, P.J.; Stephenson, N.L.; Byrne, J.C.; Daniels, L.D.; Franklin, J.F.; [et al.]. 2009. Widespread increase of tree mortality rates in the western United States. Science. 323: 521-524. https://doi.org/10.1126/science.1165000.
• Selected Readings: Litter flammability (references are US-centric, but many species that overlap to Canada and northern Mexico)
  • Varner, J.M.; Shearman, T.; Kane, J.M.; Banwell, E.M.; Jules, E.S.; Stambaugh, M.C. 2022. Understanding flammability and bark thickness in the genus Pinus using a phylogenetic approach. Nature Scientific Reports. 12: 7384. https://doi.org/10.1038/s41598-022-11451-x.*~35 pine species
  • Varner, J.M.; Kane, J.M.; Kreye, J.K.; Shearman, T.M. 2021. Litter flammability of 50 southeastern North American tree species: evidence for mesophication gradients across multiple ecosystems. Frontiers in Forests and Global Change. 4: art727042. https://doi.org/10.3389/ffgc.2021.727042.*50 species of Eastern North America
  • Stevens, J.; Kling, M.; Schwilk, D.; Varner, J.M.; Kane, J.M. 2020. Biogeography of fire regimes in western US conifer forests: a trait-based approach. Global Ecology & Biogeography. 29: 944–955. https://doi.org/10.1111/geb.13079. *Many western conifers, some overlap with Varner et al. (2021)
  • Engber, E.A.; Varner, J.M. 2012. Patterns of flammability of the California oaks: the role of leaf traits. Canadian Journal of Forest Research. 42: 1965–1975. https://doi.org/10.1139/x2012-138.*18 Pacific west oaks and oak-allies
  • Varner, J.M.; Kuljian, H.G.; Kreye, J.K. 2017. Fires without tanoak: the effects of a non-native disease on community flammability. Biological Invasions. 19: 2307–2317. https://doi.org/10.1007/s10530-017-1443-z. *Several additional western hardwoods
• Selected Readings: Fire regimes
  • Greenberg, C.H.; Collins, B. 2021. Fire ecology and management: past, present, and future of US forested ecosystems. Springer International Publishing.
  • LANDFIRE Historical Fire Regime and Vegetation Departure. https://www.landfire.gov/fireregime.php.
  • Baltzer, J.L.; Day, N.J.; Walker, X.J.; Greene, D.; Mack, M.C.; [et al.]. 2021. Increasing fire and the decline of fire adapted black spruce in the boreal forest. Proceedings of the National Academy of Sciences. 118(45): e2024872118. https://doi.org/10.1073/pnas.2024872118.
  • Rogers, B.M.; Soja, A.J.; Goulden, M.L.; Randerson, J.T. 2015. Influence of tree species on continental differences in boreal fires and climate feedbacks. Nature Geoscience. 8(3): 228–234. https://doi.org/10.1038/ngeo2352.

Section 9. Disturbance Regime: Drought

Broadly the four chapter components ask four sets of questions:

1. What do droughts look like in the range of the species? Is there a very dry period every year, or do droughts occur with interannual timescales? Is it a wet place that has dry spells or is it a dry place that misses its rainfall occasionally? What happened to the species during extreme past droughts? What does the future hold for drought?
2. How does the species respond to drought? Is it a species that has a competitive advantage under dry conditions? Is it a species that sacrifices drought resilience for growth potential, so is severely sensitive to drought? Does the relative drought sensitivity affect the local geography of the plant (e.g., more commonly found in floodplains)?
3. How does drought moderate the species’ relationship with insects, disease, and wildfire? How might these shift in the future?
4. What can/should we do to manage the species under normal or extreme droughts? How should we manage for emergent risks in a changing climate?

For additional suggested guidance, see Section 9 in the USNAP Chapter Outline, available in the folder on Box/Pinyon at https://usfs.app.box.com/folder/257907839959. 

• Selected Readings: syntheses and other key articles on drought and forests
  • Vose, J.M.; Clark, J.S.; Luce, C.H.; Patel-Weynand, T., eds. 2016. Effects of drought on forests and rangelands in the United States: a comprehensive science synthesis. Gen. Tech. Rep. WO-93b. Washington, DC: U.S. Department of Agriculture, Forest Service, Washington Office. https://research.fs.usda.gov/treesearch/50261.
  • Vose, J.M.; Peterson, D.L.; Luce, C.H.; Patel-Weynand, T., eds. 2019. Effects of drought on forests and rangelands in the United States: translating science into management responses. Gen. Tech. Rep. WO-98. Washington, DC: U.S. Department of Agriculture. Forest Service, Washington Office. 227 p. https://research.fs.usda.gov/treesearch/59158.
  • Van Loon, A.F.; Kchouk, S.; Matanó, A.; Tootoonchi, F.; Garreton, C.A.; Hassaballah, K.E; [et al.]. 2024. Review article: Drought as a continuum: memory effects in interlinked hydrological, ecological, and social systems. Natural Hazards and Earth System Sciences, NHESS. 24: 3173–3205. doi: 10.5194/nhess-24-3173-2024. https://nhess.copernicus.org/articles/24/3173/2024/.
  • Niinemets, U.; Valladeres, F. 2006. Tolerance to shade, drought and waterlogging of temperate northern hemisphere trees and shrubs. Ecological Monographs. 76(4): 521–547. A table showing shade, drought, and waterlogging tolerance rankings for 806 species of woody plants from the temperate Northern Hemisphere can be accessed at Ecological Archives M076-020-A1.
  • Clark, J.S.; Iverson, L.; Woodall, C.W.; Allen, C.D.; Bell, D.M., Bragg, D.C.; [et al.]. 2016. The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States. Global Change Biology. 22(7): 2329–2352. https://onlinelibrary.wiley.com/doi/10.1111/gcb.13160.
  • Littell, J.S.; Peterson, D.L.; Riley, K.L.; Liu, Y.; Luce, C.H. 2016. A review of the relationships between drought and forest fire in the United States. Global Change Biology. 22(7): 2353–2369. https://onlinelibrary.wiley.com/doi/10.1111/gcb.13275.
  • Schlesinger, W.H.; Dietze, M.C.; Jackson, R.B.; Phillips, R.P.; Rhoades, C.C.; Rustad, L.E.; Vose, J.M. 2016. Forest biogeochemistry in response to drought. Global Change Biology. 22(7): 2318–2328. https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.13105.
  • Luce, C.H.; Vose, J. M.; Pederson, N.; Campbell, J.; Millar, C.; Kormos, P.; Woods, R. 2016. Contributing factors for drought in United States forest ecosystems under projected future climates and their uncertainty. Forest Ecology and Management. 380: 299–308. doi: 10.1016/j.foreco.2016.05.020. https://www.sciencedirect.com/science/article/pii/S0378112716302602.
  • Phillips, R.P.; Ibáñez, I.; D’Orangeville, L.; Hanson, P.J.; Ryan, M.G.; McDowell, N.G. 2016. A belowground perspective on the drought sensitivity of forests: towards improved understanding and simulation. Forest Ecology and Management. 380: 309–320. https://www.sciencedirect.com/science/article/pii/S0378112716304662.
  • Vose, J.M.; Miniat, C.F.; Luce, C.H.; Asbjornsen, H.; Caldwell, P.V.; Campbell, J.L.; [et al.]. 2016. Ecohydrological implications of drought for forests in the United States. Forest Ecology and Management. 380: 335–345. doi:10.1016/j.foreco.2016.03.025. https://www.sciencedirect.com/science/article/pii/S0378112716301013.
  • Kolb, T.E.; Fettig, C.J.; Ayres, M.P.; Bentz, B.J.; Hicke, J.A., Mathiasen, R. [et al.]. 2016. Observed and anticipated impacts of drought on forest insects and diseases in the United States. Forest Ecology and Management. 380: 321–334. https://www.sciencedirect.com/science/article/pii/S0378112716302316.
  • Norman, S.P.; Koch, F.H.; Hargrove, W.W. 2016. Review of broad-scale drought monitoring of forests: toward an integrated data mining approach. Forest Ecology and Management. 380: 346–358. https://www.sciencedirect.com/science/article/pii/S0378112716303267.
  • Fettig, C.J.; Mortenson, L.A.; Bulaon, B.M.; Foulk, P.B. 2019. Tree mortality following drought in the central and southern Sierra Nevada, California, US. Forest Ecology and Management. 432: 164–178. https://www.sciencedirect.com/science/article/pii/S0378112718313859.
  • McDowell, N.G.; Sapes, G.; Pivovaroff, A.; Adams, H.D.; Allen, C.D., Anderegg, W.R.; [et al.]. 2022. Mechanisms of woody-plant mortality under rising drought, CO₂ and vapour pressure deficit. Nature Reviews Earth & Environment. 3(5):  294–308. https://www.nature.com/articles/s43017-022-00272-1.
  • Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; Vennetier, M.; [et al.]. 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management. 259(4): 660–684. https://www.sciencedirect.com/science/article/abs/pii/S037811270900615X.
  • Breshears, D.D.; Cobb, N.S.; Rich, P.M.; Price, K.P.; Allen, C.D.; Balice, R.G.; [et al.]. 2005. Regional vegetation die-off in response to global-change-type drought. Proceedings of the National Academy of Sciences. 102(42): 15144–15148. https://www.pnas.org/doi/abs/10.1073/pnas.0505734102.

Section 11. Goods and Services

Our increasingly urbanized modern society is distanced from forest habitats, so it is easy to forget that forests continue to provide many goods that we take for granted, as well as ecosystem services on which our society depends. Forest goods considers forest resources such as wood and non-timber products as well as the ecosystem services that forests provide. These services include carbon storage and mitigation potential, climate regulation, flood control, pollution abatement, freshwater quality and supply, and soil protection.

• Selected Readings
  • Alden, H.A. 1995. Hardwoods of North America. Gen. Tech. Rep. FPL–GTR–83. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 136 p. https://www.fs.usda.gov/treesearch/pubs/5647 https://doi.org/10.2737/FPL-GTR-83.
  • Alden, H.A. 1997. Softwoods of North America. Gen. Tech. Rep. FPL–GTR–102. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 151 p. https://www.fs.usda.gov/treesearch/pubs/5648. https://doi.org/10.2737/FPL-GTR-102.
  • Brauman, K.A.; Garibaldi, L.A.; Polasky, S.; Zayas, C.; Aumeeruddy-Thomas, Y.; [et al.]. 2019. Status and trends—nature’s contributions to people (NCP). In: Brondízio, E.S.; Settele, J.; Díaz, S.; Ngo, H.T., eds. Global assessment report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Bonn, Germany: IPBES Secretariat. Chapter 2.3.  https://doi.org/10.5281/zenodo.3832036. (See the IPBES Global assessment – Chapter 2.3 Supplementary materials).
  • Burns, R.M.; Honkala, B.H., tech. coords. 1990. Silvics of North America. 1. Conifers. Agriculture Handbook 654. U.S. Department of Agriculture, Forest Service. https://www.fs.usda.gov/treesearch/pubs/1547.
  • Burns, R.M.; Honkala, B.H., tech. coords. 1990. Silvics of North America. 2. Hardwoods. Agriculture Handbook 654. U.S. Department of Agriculture, Forest Service. https://www.fs.usda.gov/treesearch/pubs/1548.
  • Chamberlain, J.L.; Emery, M.R.; Patel-Weynand, T. 2018. Assessment of nontimber forest products in the United States under changing conditions. Gen. Tech. Rep. SRS-GTR-232. U.S. Department of Agriculture, Forest Service, Southern Research Station. 268 p. https://doi.org/10.2737/SRS-GTR-232.
  • Chamberlain, J.L. [N.d.]. Nontimber forest products factsheets. https://srs.fs.usda.gov/research/nontimber-forest-products/factsheets/.
  • Deal, R.L.; Smith, N.; Gate, J. 2020. Connecting the dots: moving frameworks for ecosystem services from USDA Forest Service national programs to forest and project scales. In: Pile, L.S.; Deal, R.L.; Dey, D.C.; Gwaze, D.; Kabrick, J.M. [et al.]., comps. The 2019 national silviculture workshop: a focus on forest management-research partnerships. Gen. Tech. Rep. NRS-P-193. Madison, WI: U.S. Department of Agriculture, Forest Service, Northern Research Station: 213–215. https://doi.org/10.2737/NRS-GTR-P-193-paper27.
  • Díaz, S.; Pascual, U.; Stenseke, M.; Martín-López, B.; Watson, R.T.; [et al.]. 2018. Assessing nature's contributions to people. Science. 359(6373): 270–273. https://www.science.org/doi/10.1126/science.aap8826. *See the Supplementary Material for Assessing nature’s contributions to people.
  • Ghazoul, J. 2015. Forest goods and services. In: Forests: a very short introduction. Oxford, UK: Oxford University Press. https://doi.org/10.1093/actrade/9780198706175.003.0005.
  • Krieger, D.J. 2001. Economic value of forest ecosystem services: a review. Washington, DC: The Wilderness Society. http://www.truevaluemetrics.org/DBpdfs/EcoSystem/The-Wilderness-Society-Ecosystem-Services-Value.pdf.
  • Merry, K.; Bettinger, P.; Siry, J.; McNulty, S.; Gavazzi, M. 2021. Forester preferences on revising Silvics of North America—a survey of registered foresters in four states. Journal of Forestry. 14(3): 1–9. https://doi.org/10.1093/jofore/fvab042.
  • U.S. Department of Agriculture, Forest Service. 2022. i-Tree®. https://www.itreetools.org/.
  • Wacker, J.P. 2021. Use of wood in buildings and bridges. In: Wood handbook—wood as an engineering material. Gen. Tech. Rep. FPL-GTR-282. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. Chapter 17. https://www.fs.usda.gov/treesearch/pubs/62265.
  • Wiemann, M.C. 2021. Characteristics and availability of commercially important woods. In: Wood handbook—wood as an engineering material. Gen. Tech. Rep. FPL-GTR-282. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. Chapter 2. https://www.fs.usda.gov/treesearch/pubs/62246.
  • Wiedenhoeft, A.; Eberhardt, T.L. 2021. Structure and function of wood. In: Wood handbook—wood as an engineering material. Gen. Tech. Rep. FPL-GTR-282. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. Chapter 3. https://www.fs.usda.gov/treesearch/pubs/62242.

Section 13. Knowledge Gaps

This section should be formatted as a bulleted list, with each knowledge gap listed briefly. 

The following glossaries have been provided to assist authors overcome any potential language barriers. Terms can be found in English, Spanish, and French.

English

• https://www.srs.fs.usda.gov/pubs/misc/ag_654/volume_2/glossary/glossary.htm
• https://www.srs.fs.usda.gov/sustain/report/appendix/glossary.htm

Spanish

• https://www.fs.usda.gov/rm/pubs_rm/rm_gtr152.pdf

French

• https://cfs.nrcan.gc.ca/terms

The following information is intended to provide metadata for the data sources and methods used to model and map individual species in the Silvics of North America. The information is provided to help authors understand how the models and maps were created and to describe what these products represent. Authors may choose to provide brief summaries of the information provided in this documentation for readers. Suggested figure captions are provided and authors can modify these as needed to describe other information depicted in the maps.

It is expected that not all species can be modeled to an acceptable accuracy. Therefore, for some species, only the recent forest inventory data will be provided and models and maps under current and potential future conditions will be omitted.

It is important to understand that the models and maps are based on the best available forest inventory data provided by Canada, Mexico, and the United States and currently accepted scientific techniques. Therefore, the information provides a snapshot in time representation of current knowledge.

Appendix A – Table of Contents

1. GETTING STARTED
   1. Guidelines to Authors
   2. Model Interpretation Disclaimer
2. FOREST INVENTORY
   1. Canada
   2. Mexico
   3. United States
   4. Species’ Abundance
3. ENVIRONMENTAL DATA
   1. Elevation
   2. Climate
4. SPECIES DISTRIBUTION MODELS
   1. Multi-Model Ensemble
   2. R-square of Models
   3. Mapped Distribution
   4. Machine Learning Algorithms
5. MIGRATION AND COLONIZATION MODEL
   1. Model and Inputs
   2. Mapped Migration Potential
6. STATISTICAL SUMMARIES
   1. Boxplots
   2. Tabular Data
7. FILE STORAGE AND ACCESSIBILITY
8. LITERATURE CITED

1. GETTING STARTED

Guidelines to Authors

We encourage authors to read this entire document to understand the data and modeling techniques used to generate the species maps that will be used in the updated Silvics of North America. However, we offer a summary to provide the basic information to get you started.

Forest Inventory data from Canada, Mexico, and the United States were combined to derive a relative importance value (RIV, also referred as relative abundance), for each species. The RIV metric ranges from 0–100, where 0 indicates the species is absent and 100 the species completely dominates the forest composition. For each species, maps are provided that show raw abundance data, current habitat quality, future habitat quality, and future migration potential. Additionally, boxplots and statistics tables are provided for interpreting the climate and elevation data associated with the actual and modeled relative abundance values. Details on how to access these files are provided at the end of this documentation. Note that updated versions of files will be added to the storage location periodically; these will be indicated by ‘v2’, ‘v3’, etc. at the end of file names. Please use the most recent file version when incorporating material into your chapter.

Model Interpretation Disclaimer

We used five machine learning algorithms to model current and potential future distributions of relative abundance for each species across North America. The consensus of the five models were averaged and mapped to indicate dominant trends in species relative abundance. Model performance is indicated by the percent R² and authors are strongly encouraged to consider this metric when describing the mapped species distribution or proposing to include the figure in the chapter.

The forest inventories are constrained to sampling protocols and may not fully capture the species’ complete range; therefore, the maps of actual relative abundance include Little’s range extent for historical context. Discrepancies between the reported inventories and Little’s range can be due to several factors.

For more details, please refer to the Species Distribution Models and Migration and Colonization Model sections.

2. FOREST INVENTORY

Canada

Species information was obtained from the photo plot portion of the National Forest Inventory (NFI 2021), which is managed by the Canadian Forest Service, Natural Resources Canada. This inventory consists of a network of 2×2 km photo plots that are distributed across the country in a regular grid. These data were collected between 2007 and 2017. The percent composition of individual species was provided to represent individual species importance or relative dominance. These data included 80 species of trees and 20 tree species identified only to genus.

Mexico

Species information was obtained from National Forest Inventory and Soils (INFyS, as described by the Spanish acronym), carried out during 2004-2009. INFyS is the national-level forest inventory program in Mexico (CONAFOR, 2004) that contains 26,220 sampling plots, systematically distributed throughout the country and has a sample density of approximately one plot per 2,577 ha of forestland. Dasometric data are reported for all sampling units describing 390 variables and metrics about species composition, tree characteristics, natural regeneration, and forest and soils condition for all living trees for a DBH greater than ~3 inches (7.5 cm). Additional information can be accessed from https://snmf.cnf.gob.mx/datos-del-inventario/.

United States

Species information was obtained from the USDA Forest Service Forest Inventory and Analysis (FIA) program. Records from the most recently completed inventory cycle for each state were extracted to calculate species abundance. A total of 979,075 plots (conterminous United States and Alaska) sampled over the period 1998 to 2018 report various metrics for individual trees. We combined the Alaska coastal inventory with the limited interior inventory for a total of 16,431 plots for Alaska. For each inventory plot, records were aggregated to species to indicate the overall basal area and number of trees greater than 5 inches (12.7 cm) in diameter at breast height. Additional information can be accessed from https://research.fs.usda.gov/programs/fia.

Species Abundance

For the FIA data, relative importance value (RIV, also referred as relative abundance), is a measure of abundance which incorporates biotic influence and is calculated from the basal area and number of stems of the overstory species (Iverson et al., 2008; Peters et al., 2019).

RIV(x) = 50 * {BA(x) / SumOf(BA(AllSpeciesInPlot))} + 50 * {NS(x) / SumOf(NS(AllSpeciesInPlot))}

Where x is the species of interest, BA is the basal area, and NS is the number of stems including both the overstory and understory trees. In monotypic stands, the RIV would reach a maximum of 100 percent.

RIV reflects the realized niche – it incorporates land-use legacies and disturbance patterns and in some cases soils that results in the relative abundance patterns detected by forest inventory sampling.

We merged the NFI photo plot data with percent composition estimates from Canada with the RIV data from FIA to obtain the full range of trees that spanned Canada and the United States. Mexican National Forest Inventory data are similar to the U.S.A.’s FIA data, so we derived RIV for Mexican data and merged them for species that spanned Mexico and the United States.

Version 2

Continentally, there are many 20 km2 grid cells that are not associated with any inventory data, creating gaps in the model training data and maps. Additionally, for U.S. FIA data, there are numerous 20 km2 grid cells that contain few inventory plots (≤3) to represent RIV of a species. In such cases, a focal window approach was used to average RIV from neighboring values. This results in better models because it reduces abundance peaks at grid cells with sparse inventory coverage, especially near the borders of species current distributions.

For the U.S. FIA data, the plot count within each 20 km2 grid was assessed and when one plot occurred, the focal window was set to a 5×5 neighborhood. When two or three plots occurred, the focal window was set to a 3×3 neighborhood. For Canadian NFI data, a 3×3 neighborhood was used since the photo plot density is much less than the FIA coverage. If the focal mean resulted in a RIV of zero (as happens for regions farther away from main distribution), the original RIV was retained.

The resulting focal mean RIV attempts to reduce data peaks when sparse numbers of inventory plots are present in U.S. FIA data and fill missing values in Canadian NFI data when no inventory samples occur within the sampling region.

3. ENVIRONMENTAL DATA

Elevation

A digital elevation model of North America having a spatial resolution of 500 m was obtained from the U.S. Geological Survey. The elevation values were aggregated to a 20 km × 20 km grid by calculating the mean value and other topographic metrics were derived. These included slope angle, slope aspect, topographic roughness, topographic position index, and topographic wetness index. The data were processed using R, RSAGA, and QGIS with standard algorithms.

Climate

Gridded current and projected future climate data for North America at a spatial resolution of 1 km², were generated using R (AdaptWest Project, 2021, R Core Team, 2022). Values for the 30-year period 1991–2020 represent the current baseline and align with the forest inventory in that the trees would have either established or experienced growing conditions during this period. Climate projections for the 2070–2099 period were based on CMIP6 outputs from eight general circulation models (GCM) and two emissions pathways (SSP2-4.5 and SSP5-8.5). The GCMs included ACCESS-ESM1.5, CNRM-ESM2-1, EC-Earth3, GFDL-ESM4, GISS-E2-1-G, MIROC6, MPI-ESM1.2-HR, and MRI-ESM2.0 for which individual projects were averaged to reduce noise and uncertainties among the models. The Shared Socioeconomic Pathways (SSP) are the latest greenhouse gas emission scenarios used by the Intergovernmental Panel on Climate Change. Further details are provided below:

The following climate metrics were used to develop the species distribution models:

The two SSP scenarios represent “middle of the road” (SSP2-4.5) and “high” (SSP5-8.5) scenarios, in which CO₂ levels rise to ~550 ppm or nearly 1,025 ppm by the end of the century, respectively. Each SSP considers various trends and trajectories of population growth, industrial developments, technologic advances, socioeconomic interactions, land-use changes, and other factors. Since the future is uncertain, these scenarios help to bracket the plausible conditions that may occur. The SSP2-4.5 estimates a future warming of 2.7 °C (~4.9 °F), globally, while the SSP5-8.5 estimates 4.4 °C (~7.9 °F).

4. SPECIES DISTRIBUTION MODELS

Multi-Model Ensemble

A multi-model ensemble (MME) of five machine learning algorithms were used to model current and potential future distributions (when feasible) of tree species relative abundance across North America (Prasad, 2018; Prasad et al., 2020). See Machine Learning Algorithms section below for details. There are several acceptable ways model ensembles can be created, and we used two: 1) an overall mean of the five models and 2) a consensus approach. For the model consensus, where all five models predicted values > 0, the mean was calculated; otherwise, a zero was used to indicate disagreement among the models. Therefore, the model consensus only represents the dominant trends in species relative abundance and attempts to minimize noise/fuzzy values from a few of the models.

All five models used climate-topo-interactions as predictors at the macroscale (e.g., 20 km² grids) and across North America, in an attempt to understand long-term spatial and temporal trends that are divested of land-use legacies and disturbance patterns. This macroscale signature reflects both the realized and fundamental niche of a species—that is, suitable habitat both within and outside current range limits. It is important to not conflate field-based observations with modeled outputs—although the models may correspond well at broad scales depending on the overall R² of the model.

R-square of Models

The R-square of the MME was calculated by averaging the R-square values of the five individual models. All predicted maps show the average R-square of the ensemble. Because R-square values depend on how well the abundance distribution is explained by the climate-topographic variables, it varies by species. Generally, R-square values below 10 percent or 15 percent can be considered as predictions with low reliability. We include predictions for all species irrespective of how low the R-square is, and let the authors decide whether or not to consider the current and future predictions in their chapter.

Mapped Distribution

The projections of relative abundance represent habitat suitability, which have been classified to represent habitat quality (HQ). The modeled HQ is based on climate-topographic interactions only since these are most relevant at a continental scale (20 km² resolution). Assumption: Over large spatial and temporal domains (historical), climate and topography have historically been the major contributors to habitat suitability.

The following maps are provided for each tree species:

1. All species which have been reported by forest inventory data will have a figure that maps the inventoried distribution. These inventoried distribution maps represent the actual relative abundance of the species where inventories have occurred.
2. For each species two figures are provided which include the modeled distribution of HQ under current conditions for the a) consensus and b) mean models; and
3. the potential future distribution of HQ under the a) SSP2-4.5 and b) SSP5-8.5 scenarios for the consensus model only.

The legend of Habitat Quality representing eight classes of relative abundance includes the following:

The percent R², averaged among the five models, is displayed on the current and future modeled habitat maps. Authors should use discretion when interpreting species with relatively low percent R² values or determine that the models are unacceptable.

Machine Learning Algorithms

The five machine learning algorithms that were implemented in R were Bagging, Extremely Randomized Trees, and Random Forest, all of which were implemented using ranger library, Extreme Gradient Boosting with the xgboost library and Stochastic Gradient Boosting with gbm library. These models were chosen because they use the same base-learners (regression-tree), but have variations which embody strengths and limitations; thus, the outputs should encompass most of the plausible range of predictions.

5. MIGRATION AND COLONIZATION MODEL

Model and Inputs

The habitat quality (HQ) model represents where a species could have suitable habitat under the environmental conditions included in the distribution model. The resulting HQ doesn’t indicate whether newly suitable habitat beyond a species current range could be colonized in the future. Trees are typically slow to migrate and may not occupy all predicted habitats in the future.

Colonization likelihoods (CL) were computed for each species which had an acceptable HQ model using a long-distance migration model that incorporates current abundance, historical migration rates and current habitat fragmentation (Schwartz, 1993). Simulation of long-distance migration was implemented via a fat-tailed inverse power function at a 1 km² cell resolution. The likelihood of an unoccupied cell becoming colonized by a particular species during each generation is a function of that species’ abundance in the surrounding cells, the habitat quality of the unoccupied cell (must have at least 10 percent forest cover (Commission for Environmental Cooperation, 2020), to ensure that it is a habitable cell) and a search window distance function. The colonization likelihood for each unoccupied cell is calculated by summing over all occupied cells at each generation. The stochastic nature of the tail of the long-distance migration is simulated by drawing a random number from an even distribution and comparing it with the calculated likelihood to determine if the cell gets colonized. Simulation using multiple historical migration rates and application to multiple species was made feasible by using convolution and Fast Fourier Transforms to keep the computation time to a minimum (Prasad et al., 2013). For this study, we use an optimistic, but historically defensible migration rate of 50 km century^-1 for all species and a generous search window of 500 km (to accommodate stochastic long-distance dispersals) as the limit for migration within each generation. These estimates are generally at the higher end of reported tree migration rates (McLachlan and Clark, 2004; McLachlan et al., 2005). We deliberately chose not to parameterize individual species migration rates owing to large uncertainties in life histories and dispersal syndromes among the many species.

A recent improvement to this model (Prasad et al., 2020) involved relaxing the requirement for a rigid source–sink boundary along the migration front by allowing it to opportunistically colonize suitable cells throughout the current range of the species. This produces a more realistic migration scenario where colonization can happen within the gaps in the current distribution (infilling or interpolating) as well as at range boundaries (migrating/ outfilling). Each species is allowed to migrate based on its current distribution and generation time with no climatic constraints, for approximately 80 years (by the end of the century). This ensures that in spite of having a common migration rate, the landscape is colonized based on individualistic species responses. The number of generations (model iterations) required to simulate ~80 years of migration varies among species depending on their generation time (which varies between 14 and 33 years for the species in this study). For example, for purposes of our models, sugar maple requires ~33 years per generation—that is time to maturity (=3 generations in 80 years), and newly colonized cells can only contribute to colonization in the next generation since the propagules have to mature. For more details on the migration model, see Prasad et al. (2013) and Schwartz et al. (2001).

NOTE: For species for which we did not get a generation time from literature, we used the value 3 since this was the most frequent and reasonable one for end-of-the-century evaluations.

Mapped Migration Potential

The habitat quality (HQ) based on relative abundance is estimated by the MME consensus model for the SSP2-4.5 and SSP5-8.5 scenarios. Outputs are scaled 0 to 100, where 0 represents absence and 100 represents the maximum where the cell contains monotypic stands of that species. These outputs were reclassified into three classes: low (1–6), medium (6–16) and high (17–100). The current distribution of relative abundance based on forest inventory (scaled 0–100) was also classified into three classes—low, medium, and high—based on the HQ categories described above. Similarly, the colonization likelihoods (CL) estimated by the migration model (scaled 0 to 100 where 0 represents no colonization and 100 the maximum colonization likelihood) for ~80 years were also reclassified into uncolonized (0), low (1–10), medium (11–30) and high (31–100). These two reclassified rasters were combined to yield 12 combination classes:

1. HQlow/CLnull
2. HQlow/CLlow
3. HQlow/ CLmed
4. HQlow/CLhi
5. HQmed/CLnull
6. HQmed/CLlow
7. HQmed/CLmed
8. HQmed/CLhi
9. HQhi/CLnull
10. HQhi/CLlow
11. HQhi/CLmed
12. HQhi/CLhi

And three occupied classes:

1. HQlow_Occupied
2. HQmed_Occupied
3. HQhi_Occupied

Note that CLnull refers to the uncolonized class. The reclassification schemes were based on heuristics after examining the HQ and CL distribution patterns of 25 tree species (Prasad et al., 2020). The color scheme (Figure 1) to depict the combination of HQ and CL classes was chosen to highlight the interplay of HQ and CL, as well as the absence of CL (CLnull) in areas where HQ is present. Areas of future predicted HQ, where CL is absent (i.e., where the species cannot migrate, CLnull), are depicted in shades of grey increasing in darkness from HQlow/CLnull, HQmed/CLnull, HQhi/CLnull in that order. If the species is currently present (according to forest inventory estimates), it is depicted as shades of green to distinguish it from future scenarios. The areas where HQ is low (HQlow) and CL increases from low to high (HQlow/CLlow, HQlow/CLmed, HQlow/CLhi) are in progressively darker shades of yellow; areas where HQ is medium (HQmed) and CL increases from low to high (HQmed/CLlow, HQmed/CLmed, HQmed/CLhi) are in progressively darker shades of blue; and where HQ is high (HQhi) and CL increases from low to high (HQhi/CLlow, HQhi/CLmed, HQhi/CLhi) are in progressively darker shades of purple. This color scheme helps distinguish those areas that are modeled to be colonized (colored) from those that are suitable, but not colonized (shades of gray), and those that are currently occupied (shades of green).

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For species with acceptable MME predictions, figures containing the mapped HQ-CL under the two future climate scenarios can be found in the species figure folder described in the Mapped Distribution subsection of Section 3. 

6. STATISTICAL SUMMARIES

Boxplots

Box-plot graphs, also called box and whisker plots, are a visual way to show the relationship of several descriptive statistics for a dataset. Included in the boxplots are the minimum, quartiles, median, and maximum values, as well as outliers values. Boxplot figures are provided for elevation (m), annual precipitation (mm), and mean annual temperature (°C) corresponding to each species mapped distribution of forest inventory data (Actual), modeled under current climate (CurMod), and the two future projections SSP2-45 and SSP5-85.

Using the elevation boxplot for balsam fir (Abies balsamea) as an example, the y-axis includes the range of elevation values while the x-axis contains the different mapped distributions. The whiskers represent the lower and upper 25 percent of values with any outliers represented as black dots. The minimum and maximum values are represented as the horizontal whiskers or the last outlier. The box, divided by a thick horizontal line at the median value, indicates the interquartile range with the ends of the box representing the lower 25 percent and the upper 25 percent of the values. For some species, the current and/or future models suggested no suitable habitat and thus, the boxplot files will not display information under these scenarios.

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Tabular Data

The statistics used to graph the boxplots are also included in a tabular format (CSV) for authors to include this table (table 2.1). The files include the minimum (Min), lower quartile (Q1), median, mean, upper quartile (Q3), and maximum (Max) values. For some species, the current and/or future models suggested no suitable habitat and thus, the statistics files will contain “NA” under these scenarios.

7. FILE STORAGE AND ACCESSIBILITY

All files are stored at the Box/Pinyon site made available to authors. Each individual species folder is named by the species scientific name. The figure file names include the source of the forest inventory data represented by Canada-US, Mexico-US, and US-Only. A master list is provided (SpeciesList_authors_rsq.xlsx) which includes 1) an index, 2) the species common name, 3) the species scientific name, 4) a species code used by the USDA Forest Service to reduce the file names, 5) a status indicating whether the species has been mapped and modeled, 6) the source of forest inventory, and 7) the percent R² for species that have been modeled. For example, the species balsam fir, Abies balsamea (species code of 12), has a natural range within Canada and the United States which is reflected in each country’s forest inventories. Therefore, authors can locate the figure files within the following hierarchy:

01_Peer Review

           01_Figures and Tables

                       Abies_balsamea (FT)

                                   Canada-US_Actual_sp12.png (Option for Figure 2.1)

                                   Canada-US_CurrentPredicted_Consensus_sp12.png (Option for Figure 2.1)

                                   Canada-US_HQCL_Consensus_sp12.png (Figure 2.2a and 2.2b)

The boxplots and tables are similarly stored and accessed. Note that updated file versions will be identified with ‘_v2’, ‘_v3’, etc. at the end of file names. Please use the most recent version in your chapter. For some species version 2 (‘_v2’) files may not exist and for others version 1 files may not have been processed. We suggest that when possible, version 2 files be used.

8. LITERATURE CITED

• AdaptWest Project. 2021. Gridded current and projected climate data for North America at 1km resolution, generated using the ClimateNA v7.01 software (T. Wang et al., 2021). Available at https://adaptwest.databasin.org/pages/adaptwest-climatenav71/.
• Commission for Environmental Cooperation (CEC). 2020. 2015 Land cover of North America at 30 meters. North American Land Change Monitoring System. Canada Centre for Remote Sensing (CCRS), U.S. Geological Survey (USGS), Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO), Comisión Nacional Forestal (CONAFOR), Instituto Nacional de Estadística y Geografía (INEGI).
• Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters, M. 2008. Estimating potential habitat for 134 eastern US tree species under six climate scenarios. Forest Ecology and Management. 254(3): 390–406. https://doi.org/10.1016/j.foreco.2007.07.023.
• McLachlan, J.S.; Clark, J.S.; Manos, P.S. 2005. Molecular indicators of tree migration capacity under rapid climate change. Ecology, 86(8), 2088–2098. https://doi.org/10.1890/04-1036.
• McLachlan, J.S.; Clark, J.S. 2004. Reconstructing historical ranges with fossil data at continental scales. Forest Ecology and Management. 197(1–3): 139–147. https://doi.org/10.1016/j.foreco.2004.05.026.
• National Forest Inventory [NFI]. 2021. Canada’s National Forest Inventory—first remeasurement (2007–2017) photo-plot data, version 1.0.
• Peters, M.P.; Iverson, L.R.; Prasad, A.M.; Matthews, S.N. 2019. Utilizing the density of inventory samples to define a hybrid lattice for species distribution models: DISTRIB‐II for 135 eastern U.S. trees. Ecology and Evolution. https://doi.org/10.1002/ece3.5445.
• Prasad, A.; Pedlar, J.; Peters, M.; McKenney, D.; Iverson, L.; Matthews, S.; Adams, B. 2020. Combining US and Canadian forest inventories to assess habitat suitability and migration potential of 25 tree species under climate change. Diversity and Distributions. https://doi.org/10.1111/ddi.13078.
• Prasad, A.M. 2018. Machine learning for macroscale ecological niche modeling—a multi-model, multi-response ensemble technique for tree species management under climate change. In: Humphries, G.R.; Magness, D.R.; Huettmann, F., eds. Machine learning for ecology and sustainable natural resource management. Springer International Publishing. https://doi.org/10.1007/978-3-319-96978-7.
• Prasad, A.M.; Gardiner, J.D.; Iverson, L.R.; Matthews, S.N.; Peters, M. 2013. Exploring tree species colonization potentials using a spatially explicit simulation model: Implications for four oaks under climate change. Global Change Biology. 19(7): 2196–2208. https://doi.org/10.1111/gcb.12204.
• R Core Team. 2022. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
• Schwartz, M.W. 1992. Modelling effects of habitat fragmentation on the ability of trees to respond to climatic warming. Biodiversity and Conservation. 2(1): 51–61. https://doi.org/10.1007/BF00055102.
• Schwartz, M.W.; Iverson, L.R.; Prasad, A.M. 2001. Predicting the potential future distribution of four tree species in Ohio using current habitat availability and climatic forcing. Ecosystems. 4(6): 568–581. https://doi.org/10.1007/s10021-001-0030-3.