Mapping distribution of woody plant species richness from field rapid assessment and machine learning

Bo Hao Perng; Tzeng Yih Lam; Su Ting Cheng; Sheng Hsin Su; Kristina J. Anderson-Teixeira; Norman A. Bourg; David F.R.P. Burslem; Nicolas Castaño; Álvaro Duque; Sisira Ediriweera; Nimal Gunatilleke; James A. Lutz; William J. McShea; Mohamad Danial M.D. Sabri; Vojtech Novotny; Michael J. O'brien; Glen Reynolds; George D. Weiblen; Daniel Zuleta

doi:10.6165/tai.2024.69.1

Mapping distribution of woody plant species richness from field rapid assessment and machine learning

Bo Hao Perng, Tzeng Yih Lam^*, Su Ting Cheng, Sheng Hsin Su, Kristina J. Anderson-Teixeira, Norman A. Bourg, David F.R.P. Burslem, Nicolas Castaño, Álvaro Duque, Sisira Ediriweera, Nimal Gunatilleke, James A. Lutz, William J. McShea, Mohamad Danial M.D. Sabri, Vojtech Novotny, Michael J. O'brien, Glen Reynolds, George D. Weiblen, Daniel Zuleta

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Sustainable forest management needs information on spatial distribution of species richness. The objectives of this study were to understand whether knowledge, method, and effort of a rapid assessment affected accuracy and consistency in mapping species richness. A simulation study was carried out with nine 25–50 ha census plots located in tropical, subtropical, and temperate zones. Each forest site was first tessellated into non-overlapping cells. Rapid assessment was conducted in all cells to generate a complete coverage of proxies of the underlying species richness. Cells were subsampled for census, where all plant individuals were identified to species in these census cells. An artificial neural network model was built using the census cells that contain rapid assessment and census information. The model then predicted species richness of cells that were not censused. Results showed that knowledge level did not improve the accuracy and consistency in mapping species richness. Rapid assessment effort and method significantly affected the accuracy and consistency. Increasing rapid assessment effort from 10 to 40 plant individuals could improve the accuracy and consistency up to 2.2% and 2.8%, respectively. Transect reduced accuracy and consistency by up to 0.5% and 0.8%, respectively. This study suggests that knowing at least half of the species in a forest is sufficient for a rapid assessment. At least 20 plant individuals per cell is recommended for rapid assessment. Lastly, a rapid assessment could be carried out by local communities that are familiar with their forests; thus, further supporting sustainable forest management.

Original language	English
Pages (from-to)	1-15
Number of pages	15
Journal	Taiwania
Volume	69
Issue number	1
DOIs	https://doi.org/10.6165/tai.2024.69.1
Publication status	Published - 1 Jan 2024

Bibliographical note

Funding Information:
We would like to thank two anonymous reviewers for their valuable comments that have significantly improved the manuscript. The funding of this project is provided by the National Science and Technology Council Taiwan (Grant no. MOST 111-2628-B-002-042 and MOST 111-2326-B-002-005-MY3). Amacayacu: The 25-ha Long-Term Ecological Research Project of Amacayacu is a collaborative project of the Instituto Amazónico de Investigaciones Científicas Sinchi and the Universidad Nacional de Colombia Sede Medellín, in parternship with the Unidad de Manejo Especial de Parques Naturales Nacionales and the Forest Global Earth Observatory of the Smithsonian Tropical Research Institute (ForestGEO). The Amacayacu Forest Dynamics Plot is part of ForestGEO a global network of large-scale demographic tree plots. We acknowledge the Director and staff of the Amacayacu National Park for supporting and maintaining the project in this National Park. BCI: The BCI forest dynamics research project was made possible by National Science Foundation grants to Stephen P. Hubbell: DEB-0640386, DEB-0425651, DEB-0346488, DEB-0129874, DEB-00753102, DEB-9909347, DEB-9615226, DEB-9615226, DEB-9405933, DEB-9221033, DEB-9100058, DEB-8906869, DEB-8605042, DEB-8206992, DEB-7922197, support from the Forest Global Earth Observatory, the Smithsonian Tropical Research Institute, the John D. and Catherine T. MacArthur Foundation, the Mellon Foundation, the Small World Institute Fund, and numerous private individuals, and through the hard work of over 100 people from 10 countries over the past three decades. The plot project is part the Forest Global Earth Observatory (ForestGEO), a global network of large-scale demographic tree plots. Danum: The Danum plot is a core project of the Southeast Asia Rain Forest Research Partnership (SEARRP). We thank SEARRP partners, especially Yayasan Sabah for their support, and HSBC Malaysia and the University of Zurich for funding. We are grateful to the research assistants who are conducting the census, in particular the team leader Alex Karolus, and to Mike Bernados and Bill McDonald for species identifications. We thank Stuart Davies and Shameema Esufali for advice and training. Fushan: The establishment and first census of the Fushan 25-ha plot is a collaborative project by the Taiwan Forestry Research Institute (data provider), the Taiwan Forestry Bureau, the National Taiwan University (Institute of Ecology and Evolutionary Biology) and the ForestGEO (formerly the CTFS). We thank the agencies for providing the datasets and all field staff. S.H.S. also thanks Dr. I-Fang Sun for his long-term support for the Fushan project. Pasoh: Data from the Pasoh Research Forest was provided by the Forest Research Institute Malaysia-Forest Global Earth Observatory, Smithsonian Tropical Research Institute collaborative research project. Negeri Sembilan Forestry Department is the custodian of Pasoh Research Forest and I/we acknowledge the department for preserving the research forest. SCBI: Funding for the Smithsonian Conservation Biology Institute (SCBI) Large Forest Dynamics Plot (LFDP) was provided by the Smithsonian Institution, the National Zoological Park, and the HSBC Climate Partnership. Sinharaja: The 25-ha Long-Term Ecological Research Project at Sinharaja World Heritage Site is a collaborative project of the Uva Wellassa University, University of Peradeniya, the Forest Global Earth Observatory (ForestGEO) of the Smithsonian Tropical Research Institute, with supplementary funding received from the John D. and Catherine T. Macarthur Foundation, the National Institute for Environmental Science, Japan, and the Helmholtz Centre for Environmental Research-UFZ, Germany, for past censuses. The PIs gratefully acknowledge the Forest Department, Uva Wellassa University, and the Post-Graduate Institute of Science at the University of Peradeniya, Sri Lanka for supporting this project, and the local field and lab staff who tirelessly contributed in the repeated censuses of this plot. Wanang: The 50-ha Wanang Forest Dynamics Plot is a collaborative project of the New Guinea Binatang Research Center, the Forest Global Earth Observatory (ForestGEO) of the Smithsonian Tropical Research Institute, the Forest Research Institute of Papua New Guinea, the Czech Academy of Sciences (19-28126X) and the University of Minnesota supported by NSF DEB-1027297 and NIH ICBG 5UO1TW006671. We acknowledge the government of Papua New Guinea and the customary landowners of Wanang for supporting and maintaining the plot. Wind River: The Wind River Forest Dynamics Plot is a collaborative project of Utah State University and the USDA Forest Service Pacific Northwest Research Station. Funding has been provided by the Smithsonian ForestGEO, Utah State University, the Utah Agricultural Experiment Station (projects 1153, 1398, and 1423), the National Science Foundation (DEB #1542681), and private donors. We acknowledge the Gifford Pinchot National Forest and the U.S. Forest Service Wind River Field Station for providing logistical support, and the students, volunteers and staff individually listed at http://wfdp.org for data collection. Research was performed under long-term research permits issued by the USFS with validity from 2010 – 2040.

Keywords

artificial neural network
forest planning
rapid biodiversity assessment
sustainable forest management

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Cite this

Perng, B. H., Lam, T. Y., Cheng, S. T., Su, S. H., Anderson-Teixeira, K. J., Bourg, N. A., Burslem, D. F. R. P., Castaño, N., Duque, Á., Ediriweera, S., Gunatilleke, N., Lutz, J. A., McShea, W. J., Sabri, M. D. M. D., Novotny, V., O'brien, M. J., Reynolds, G., Weiblen, G. D., & Zuleta, D. (2024). Mapping distribution of woody plant species richness from field rapid assessment and machine learning. Taiwania, 69(1), 1-15. https://doi.org/10.6165/tai.2024.69.1

Perng, BH, Lam, TY, Cheng, ST, Su, SH, Anderson-Teixeira, KJ, Bourg, NA, Burslem, DFRP, Castaño, N, Duque, Á, Ediriweera, S, Gunatilleke, N, Lutz, JA, McShea, WJ, Sabri, MDMD, Novotny, V, O'brien, MJ, Reynolds, G, Weiblen, GD & Zuleta, D 2024, 'Mapping distribution of woody plant species richness from field rapid assessment and machine learning', Taiwania, vol. 69, no. 1, pp. 1-15. https://doi.org/10.6165/tai.2024.69.1

@article{ad3ae1ab4d704a2591e80a74919e7d7d,

title = "Mapping distribution of woody plant species richness from field rapid assessment and machine learning",

abstract = "Sustainable forest management needs information on spatial distribution of species richness. The objectives of this study were to understand whether knowledge, method, and effort of a rapid assessment affected accuracy and consistency in mapping species richness. A simulation study was carried out with nine 25–50 ha census plots located in tropical, subtropical, and temperate zones. Each forest site was first tessellated into non-overlapping cells. Rapid assessment was conducted in all cells to generate a complete coverage of proxies of the underlying species richness. Cells were subsampled for census, where all plant individuals were identified to species in these census cells. An artificial neural network model was built using the census cells that contain rapid assessment and census information. The model then predicted species richness of cells that were not censused. Results showed that knowledge level did not improve the accuracy and consistency in mapping species richness. Rapid assessment effort and method significantly affected the accuracy and consistency. Increasing rapid assessment effort from 10 to 40 plant individuals could improve the accuracy and consistency up to 2.2% and 2.8%, respectively. Transect reduced accuracy and consistency by up to 0.5% and 0.8%, respectively. This study suggests that knowing at least half of the species in a forest is sufficient for a rapid assessment. At least 20 plant individuals per cell is recommended for rapid assessment. Lastly, a rapid assessment could be carried out by local communities that are familiar with their forests; thus, further supporting sustainable forest management.",

keywords = "artificial neural network, forest planning, rapid biodiversity assessment, sustainable forest management",

author = "Perng, {Bo Hao} and Lam, {Tzeng Yih} and Cheng, {Su Ting} and Su, {Sheng Hsin} and Anderson-Teixeira, {Kristina J.} and Bourg, {Norman A.} and Burslem, {David F.R.P.} and Nicolas Casta{\~n}o and {\'A}lvaro Duque and Sisira Ediriweera and Nimal Gunatilleke and Lutz, {James A.} and McShea, {William J.} and Sabri, {Mohamad Danial M.D.} and Vojtech Novotny and O'brien, {Michael J.} and Glen Reynolds and Weiblen, {George D.} and Daniel Zuleta",

note = "Funding Information: We would like to thank two anonymous reviewers for their valuable comments that have significantly improved the manuscript. The funding of this project is provided by the National Science and Technology Council Taiwan (Grant no. MOST 111-2628-B-002-042 and MOST 111-2326-B-002-005-MY3). Amacayacu: The 25-ha Long-Term Ecological Research Project of Amacayacu is a collaborative project of the Instituto Amaz{\'o}nico de Investigaciones Cient{\'i}ficas Sinchi and the Universidad Nacional de Colombia Sede Medell{\'i}n, in parternship with the Unidad de Manejo Especial de Parques Naturales Nacionales and the Forest Global Earth Observatory of the Smithsonian Tropical Research Institute (ForestGEO). The Amacayacu Forest Dynamics Plot is part of ForestGEO a global network of large-scale demographic tree plots. We acknowledge the Director and staff of the Amacayacu National Park for supporting and maintaining the project in this National Park. BCI: The BCI forest dynamics research project was made possible by National Science Foundation grants to Stephen P. Hubbell: DEB-0640386, DEB-0425651, DEB-0346488, DEB-0129874, DEB-00753102, DEB-9909347, DEB-9615226, DEB-9615226, DEB-9405933, DEB-9221033, DEB-9100058, DEB-8906869, DEB-8605042, DEB-8206992, DEB-7922197, support from the Forest Global Earth Observatory, the Smithsonian Tropical Research Institute, the John D. and Catherine T. MacArthur Foundation, the Mellon Foundation, the Small World Institute Fund, and numerous private individuals, and through the hard work of over 100 people from 10 countries over the past three decades. The plot project is part the Forest Global Earth Observatory (ForestGEO), a global network of large-scale demographic tree plots. Danum: The Danum plot is a core project of the Southeast Asia Rain Forest Research Partnership (SEARRP). We thank SEARRP partners, especially Yayasan Sabah for their support, and HSBC Malaysia and the University of Zurich for funding. We are grateful to the research assistants who are conducting the census, in particular the team leader Alex Karolus, and to Mike Bernados and Bill McDonald for species identifications. We thank Stuart Davies and Shameema Esufali for advice and training. Fushan: The establishment and first census of the Fushan 25-ha plot is a collaborative project by the Taiwan Forestry Research Institute (data provider), the Taiwan Forestry Bureau, the National Taiwan University (Institute of Ecology and Evolutionary Biology) and the ForestGEO (formerly the CTFS). We thank the agencies for providing the datasets and all field staff. S.H.S. also thanks Dr. I-Fang Sun for his long-term support for the Fushan project. Pasoh: Data from the Pasoh Research Forest was provided by the Forest Research Institute Malaysia-Forest Global Earth Observatory, Smithsonian Tropical Research Institute collaborative research project. Negeri Sembilan Forestry Department is the custodian of Pasoh Research Forest and I/we acknowledge the department for preserving the research forest. SCBI: Funding for the Smithsonian Conservation Biology Institute (SCBI) Large Forest Dynamics Plot (LFDP) was provided by the Smithsonian Institution, the National Zoological Park, and the HSBC Climate Partnership. Sinharaja: The 25-ha Long-Term Ecological Research Project at Sinharaja World Heritage Site is a collaborative project of the Uva Wellassa University, University of Peradeniya, the Forest Global Earth Observatory (ForestGEO) of the Smithsonian Tropical Research Institute, with supplementary funding received from the John D. and Catherine T. Macarthur Foundation, the National Institute for Environmental Science, Japan, and the Helmholtz Centre for Environmental Research-UFZ, Germany, for past censuses. The PIs gratefully acknowledge the Forest Department, Uva Wellassa University, and the Post-Graduate Institute of Science at the University of Peradeniya, Sri Lanka for supporting this project, and the local field and lab staff who tirelessly contributed in the repeated censuses of this plot. Wanang: The 50-ha Wanang Forest Dynamics Plot is a collaborative project of the New Guinea Binatang Research Center, the Forest Global Earth Observatory (ForestGEO) of the Smithsonian Tropical Research Institute, the Forest Research Institute of Papua New Guinea, the Czech Academy of Sciences (19-28126X) and the University of Minnesota supported by NSF DEB-1027297 and NIH ICBG 5UO1TW006671. We acknowledge the government of Papua New Guinea and the customary landowners of Wanang for supporting and maintaining the plot. Wind River: The Wind River Forest Dynamics Plot is a collaborative project of Utah State University and the USDA Forest Service Pacific Northwest Research Station. Funding has been provided by the Smithsonian ForestGEO, Utah State University, the Utah Agricultural Experiment Station (projects 1153, 1398, and 1423), the National Science Foundation (DEB #1542681), and private donors. We acknowledge the Gifford Pinchot National Forest and the U.S. Forest Service Wind River Field Station for providing logistical support, and the students, volunteers and staff individually listed at http://wfdp.org for data collection. Research was performed under long-term research permits issued by the USFS with validity from 2010 – 2040. ",

year = "2024",

month = jan,

day = "1",

doi = "10.6165/tai.2024.69.1",

language = "English",

volume = "69",

pages = "1--15",

journal = "Taiwania",

issn = "0372-333X",

publisher = "National Taiwan University (IEEB)",

number = "1",

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TY - JOUR

T1 - Mapping distribution of woody plant species richness from field rapid assessment and machine learning

AU - Perng, Bo Hao

AU - Lam, Tzeng Yih

AU - Cheng, Su Ting

AU - Su, Sheng Hsin

AU - Anderson-Teixeira, Kristina J.

AU - Bourg, Norman A.

AU - Burslem, David F.R.P.

AU - Castaño, Nicolas

AU - Duque, Álvaro

AU - Ediriweera, Sisira

AU - Gunatilleke, Nimal

AU - Lutz, James A.

AU - McShea, William J.

AU - Sabri, Mohamad Danial M.D.

AU - Novotny, Vojtech

AU - O'brien, Michael J.

AU - Reynolds, Glen

AU - Weiblen, George D.

AU - Zuleta, Daniel

N1 - Funding Information: We would like to thank two anonymous reviewers for their valuable comments that have significantly improved the manuscript. The funding of this project is provided by the National Science and Technology Council Taiwan (Grant no. MOST 111-2628-B-002-042 and MOST 111-2326-B-002-005-MY3). Amacayacu: The 25-ha Long-Term Ecological Research Project of Amacayacu is a collaborative project of the Instituto Amazónico de Investigaciones Científicas Sinchi and the Universidad Nacional de Colombia Sede Medellín, in parternship with the Unidad de Manejo Especial de Parques Naturales Nacionales and the Forest Global Earth Observatory of the Smithsonian Tropical Research Institute (ForestGEO). The Amacayacu Forest Dynamics Plot is part of ForestGEO a global network of large-scale demographic tree plots. We acknowledge the Director and staff of the Amacayacu National Park for supporting and maintaining the project in this National Park. BCI: The BCI forest dynamics research project was made possible by National Science Foundation grants to Stephen P. Hubbell: DEB-0640386, DEB-0425651, DEB-0346488, DEB-0129874, DEB-00753102, DEB-9909347, DEB-9615226, DEB-9615226, DEB-9405933, DEB-9221033, DEB-9100058, DEB-8906869, DEB-8605042, DEB-8206992, DEB-7922197, support from the Forest Global Earth Observatory, the Smithsonian Tropical Research Institute, the John D. and Catherine T. MacArthur Foundation, the Mellon Foundation, the Small World Institute Fund, and numerous private individuals, and through the hard work of over 100 people from 10 countries over the past three decades. The plot project is part the Forest Global Earth Observatory (ForestGEO), a global network of large-scale demographic tree plots. Danum: The Danum plot is a core project of the Southeast Asia Rain Forest Research Partnership (SEARRP). We thank SEARRP partners, especially Yayasan Sabah for their support, and HSBC Malaysia and the University of Zurich for funding. We are grateful to the research assistants who are conducting the census, in particular the team leader Alex Karolus, and to Mike Bernados and Bill McDonald for species identifications. We thank Stuart Davies and Shameema Esufali for advice and training. Fushan: The establishment and first census of the Fushan 25-ha plot is a collaborative project by the Taiwan Forestry Research Institute (data provider), the Taiwan Forestry Bureau, the National Taiwan University (Institute of Ecology and Evolutionary Biology) and the ForestGEO (formerly the CTFS). We thank the agencies for providing the datasets and all field staff. S.H.S. also thanks Dr. I-Fang Sun for his long-term support for the Fushan project. Pasoh: Data from the Pasoh Research Forest was provided by the Forest Research Institute Malaysia-Forest Global Earth Observatory, Smithsonian Tropical Research Institute collaborative research project. Negeri Sembilan Forestry Department is the custodian of Pasoh Research Forest and I/we acknowledge the department for preserving the research forest. SCBI: Funding for the Smithsonian Conservation Biology Institute (SCBI) Large Forest Dynamics Plot (LFDP) was provided by the Smithsonian Institution, the National Zoological Park, and the HSBC Climate Partnership. Sinharaja: The 25-ha Long-Term Ecological Research Project at Sinharaja World Heritage Site is a collaborative project of the Uva Wellassa University, University of Peradeniya, the Forest Global Earth Observatory (ForestGEO) of the Smithsonian Tropical Research Institute, with supplementary funding received from the John D. and Catherine T. Macarthur Foundation, the National Institute for Environmental Science, Japan, and the Helmholtz Centre for Environmental Research-UFZ, Germany, for past censuses. The PIs gratefully acknowledge the Forest Department, Uva Wellassa University, and the Post-Graduate Institute of Science at the University of Peradeniya, Sri Lanka for supporting this project, and the local field and lab staff who tirelessly contributed in the repeated censuses of this plot. Wanang: The 50-ha Wanang Forest Dynamics Plot is a collaborative project of the New Guinea Binatang Research Center, the Forest Global Earth Observatory (ForestGEO) of the Smithsonian Tropical Research Institute, the Forest Research Institute of Papua New Guinea, the Czech Academy of Sciences (19-28126X) and the University of Minnesota supported by NSF DEB-1027297 and NIH ICBG 5UO1TW006671. We acknowledge the government of Papua New Guinea and the customary landowners of Wanang for supporting and maintaining the plot. Wind River: The Wind River Forest Dynamics Plot is a collaborative project of Utah State University and the USDA Forest Service Pacific Northwest Research Station. Funding has been provided by the Smithsonian ForestGEO, Utah State University, the Utah Agricultural Experiment Station (projects 1153, 1398, and 1423), the National Science Foundation (DEB #1542681), and private donors. We acknowledge the Gifford Pinchot National Forest and the U.S. Forest Service Wind River Field Station for providing logistical support, and the students, volunteers and staff individually listed at http://wfdp.org for data collection. Research was performed under long-term research permits issued by the USFS with validity from 2010 – 2040.

PY - 2024/1/1

Y1 - 2024/1/1

N2 - Sustainable forest management needs information on spatial distribution of species richness. The objectives of this study were to understand whether knowledge, method, and effort of a rapid assessment affected accuracy and consistency in mapping species richness. A simulation study was carried out with nine 25–50 ha census plots located in tropical, subtropical, and temperate zones. Each forest site was first tessellated into non-overlapping cells. Rapid assessment was conducted in all cells to generate a complete coverage of proxies of the underlying species richness. Cells were subsampled for census, where all plant individuals were identified to species in these census cells. An artificial neural network model was built using the census cells that contain rapid assessment and census information. The model then predicted species richness of cells that were not censused. Results showed that knowledge level did not improve the accuracy and consistency in mapping species richness. Rapid assessment effort and method significantly affected the accuracy and consistency. Increasing rapid assessment effort from 10 to 40 plant individuals could improve the accuracy and consistency up to 2.2% and 2.8%, respectively. Transect reduced accuracy and consistency by up to 0.5% and 0.8%, respectively. This study suggests that knowing at least half of the species in a forest is sufficient for a rapid assessment. At least 20 plant individuals per cell is recommended for rapid assessment. Lastly, a rapid assessment could be carried out by local communities that are familiar with their forests; thus, further supporting sustainable forest management.

AB - Sustainable forest management needs information on spatial distribution of species richness. The objectives of this study were to understand whether knowledge, method, and effort of a rapid assessment affected accuracy and consistency in mapping species richness. A simulation study was carried out with nine 25–50 ha census plots located in tropical, subtropical, and temperate zones. Each forest site was first tessellated into non-overlapping cells. Rapid assessment was conducted in all cells to generate a complete coverage of proxies of the underlying species richness. Cells were subsampled for census, where all plant individuals were identified to species in these census cells. An artificial neural network model was built using the census cells that contain rapid assessment and census information. The model then predicted species richness of cells that were not censused. Results showed that knowledge level did not improve the accuracy and consistency in mapping species richness. Rapid assessment effort and method significantly affected the accuracy and consistency. Increasing rapid assessment effort from 10 to 40 plant individuals could improve the accuracy and consistency up to 2.2% and 2.8%, respectively. Transect reduced accuracy and consistency by up to 0.5% and 0.8%, respectively. This study suggests that knowing at least half of the species in a forest is sufficient for a rapid assessment. At least 20 plant individuals per cell is recommended for rapid assessment. Lastly, a rapid assessment could be carried out by local communities that are familiar with their forests; thus, further supporting sustainable forest management.

KW - artificial neural network

KW - forest planning

KW - rapid biodiversity assessment

KW - sustainable forest management

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U2 - 10.6165/tai.2024.69.1

DO - 10.6165/tai.2024.69.1

M3 - Article

AN - SCOPUS:85185963981

SN - 0372-333X

VL - 69

SP - 1

EP - 15

JO - Taiwania

JF - Taiwania

IS - 1

ER -

Mapping distribution of woody plant species richness from field rapid assessment and machine learning

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