Designing Cluster Plots for Sampling Local Plant Species Composition for Biodiversity Management

  • Christie Quon University of British Columbia, Faculty of Forestry, Forest Sciences Centre, Vancouver.
  • Tzeng-Yih Lam National Taiwan University, School of Forestry and Resource Conservation, Taipei.
  • Ho-Tung Lin National Taiwan University, School of Forestry and Resource Conservation, Taipei.


Aim of study: Cluster plot designs are widely used in national forest inventory systems to assess current forest resources. By spreading subplots apart, a cluster plot could potentially capture a large variety of local plant species. This aspect has rarely been examined in the past. This study is conducted to understand how design factors of a cluster plot affect estimates of local plant species composition.

Area of study: Two large census forest plots in Taiwan and Peninsular Malaysia over 25 ha with different species richness were used.

Material and methods: Design factors of a cluster plot were plot configuration (PCONFIG), plot area (PAREA), cluster layout (CLAYOUT), and extent of ground area covered by a cluster (CEXTENT). Jaccard and Sørensen similarity indices were used to compare species compositional similarity between two cluster plot designs. A simulation study was carried out.

Main results: Results were consistent among the study sites and similarity indices. PAREA, CLAYOUT, and CEXTENT notably influenced how species composition was sampled. Larger PAREA increased similarity in species composition between two cluster plot designs. Square and rectangle CLAYOUT had the most dissimilar species composition between them. Larger CEXTENT decreased similarity in species composition.

Research highlights: We recommend that for CEXTENT ≤ 1000 m2 and PAREA ≤ 500 m2, a cluster plot of rectangle CLAYOUT is preferred for information gain. The study could potentially benefit forest managers designing cluster plots for plant diversity assessment.

Keywords: Biodiversity assessment; composition similarity; national forest inventory; species diversity; sampling design; sampling efficiency.

Abbreviation used: extent of ground area covered by a cluster (CEXTENT); cluster layout (CLAYOUT); Jaccard similarity index (JAC); plot area (PAREA); plot configuration (PCONFIG); Sørensen similarity index (SOR).


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Author Biography

Tzeng-Yih Lam, National Taiwan University, School of Forestry and Resource Conservation, Taipei.

Associate Professor in Forest Mensuration

School of Forestry and Resource Conservation

National Taiwan University


Barwell LJ, Isaac NJB, Kunin WE, 2015. Measuring β‐diversity with species abundance data. J Anim Ecol 84: 1112-1122.

Bechtold WA, Patterson PL, 2005. The enhanced forest inventory and analysis program - national sampling design and estimation procedures. General Technical Report No. SRS-80. U.S. Department of Agriculture, Forest Service, Southern Research Station, Asheville, NC, USA. 85 pp.

Burley J, Gauld I, 1995. Measuring and monitoring forest biodiversity: A commentary. IUFRO Symp of Measuring and Monitoring Biodiversity in Tropical and Temperate Forests, Bongor (Indonesia), August 27 - September 2. pp: 19-46.

Chao A, Chazdon RL, Colwell RK, Shen T-J, 2005. A new statistical approach for assessing similarity of species composition with incidence and abundance data. Ecol Lett 8: 148-159.

Condit R, Hubbell SP, Lafrankie JV, Sukumar R, Manokaran N, Foster RB, Ashton PS, 1996. Species-area and species-individual relationships for tropical trees: a comparison of three 50-ha plots. J Ecol 84: 549-562.

Costello MJ, May RM, Stork NE, 2013. Can we name Earth's species before they go extinct? Science 339: 413-416.

Ducey MJ, Gove JH, Valentine HT, 2004. A walkthrough solution to the boundary overlap problem. For Sci 50: 427-435.

FAO (Food and Agriculture Organization of the United Nations), PAR (Platform for Agrobiodiversity Research), 2011. Biodiversity for food and agriculture: contributing to food security and sustainability in a changing world. Proc of an Expert Workshop, April 14-16 April 2010, Rome, Italy.

Feeley KJ, Davies SJ, Perez R, Hubbell SP, Foster RB, 2011. Directional changes in the species composition of a tropical forest. Ecology 92: 871-882.

Fowler N, 1986. The role of competition in plant communities in arid and semiarid regions. Annu Rev Ecol Syst 17: 89-110.

Geijzendorffer IR, Regan EC, Pereira HM, Brotons L, Brummitt N, Gavish Y, Haase P, Martin CS, Mihoub J-B, Secades C et al., 2016. Bridging the gap between biodiversity data and policy reporting needs: An essential biodiversity variables perspective. J Appl Ecol 53: 1341-1350.

Green RH, Young RC, 1993. Sampling to detect rare species. Ecol Appl 3: 351-356.

Grussu G, Testolin R, Saulei S, Farcomeni A, Yosi CK, Sanctis MD, Attorre F, 2016. Optimum plot and sample sizes for carbon stock and biodiversity estimation in the lowland tropical forests of Papua New Guinea. Forestry 89: 150-158.

Güler B, Jentsch A, Apostolova I, Bartha S, Bloor JMG, Campetella G, Canullo R, Házi J, Kreyling J, Pottier J et al., 2016. How plot shape and spatial arrangement affect plant species richness counts: implications for sampling design and rarefaction analyses. J Veg Sci 27: 692-703.

Hillebrand H, Blasius B, Borer ET, Chase JM, Downing JA, Eriksson BK, Filstrup CT, Harpole WS, Hodapp D, Larsen S et al, 2018. Biodiversity change is uncoupled from species richness trends: Consequences for conservation and monitoring. J Appl Ecol 55: 169-184.

IPBES (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services), 2018. Biodiversity and nature's contributions continue dangerous decline, scientists warn.

Kershaw JA, Ducey MJ, Beers TW, Husch B, 2016. Forest Mensuration. 5th ed. John Wiley & Sons Ltd, West Sussex, UK. 613 pp.

Kleinn C, 1994. Comparison of the performance of line sampling to other forms of cluster sampling. For Ecol Manag 68: 365-373.

Kleinn C, 1996. Ein Vergleich der Effizienz von verschiedenen Clusterformen in forstlichen Großrauminventuren. Forstwiss Cent 115: 378-390.

Kleinn C, Corrales L, Morales D, 2002. Forest area in Costa Rica: A comparative study of tropical forest cover estimates over time. Environ Monit Assess 73: 17-40.

Korhonen K, Maltamo M, 1991. The evaluation of forest inventory designs using correlation functions. Silva Fenn 25: 77-83.

Lam TY, Kleinn C, 2008. Estimation of tree species richness from large area forest inventory data: Evaluation and comparison of jackknife estimators. For Ecol Manag 255: 1002-1010.

Lande R, 1996. Statistics and partitioning of species diversity, and similarity among multiple communities. Oikos 76: 5-13.

Lynch TB, 2017. Optimal plot size or point sample factor for a fixed total cost using the Fairfield Smith relation of plot size to variance. Forestry 90: 211-218.

Magnussen S, Smith B, Kleinn C, Sun IF, 2010. An urn model for species richness estimation in quadrat sampling from fixed-area populations. Forestry 83: 293-306.

Magurran AE., 2004. Measuring Biological Diversity. 1st ed. Blackwell Science Ltd., Oxford, UK. 256 pp.

Malanson GP, 1985. Spatial autocorrelation and distributions of plant species on environmental gradients. Oikos 45: 278-280.

Manokaran N, LaFrankie JV, 1990. Stand structure of Pasoh Forest Reserve, a lowland rain forest in Peninsular Malaysia. J Trop For Sci 3: 14-24.

Motz K, Sterba H, Pommerening A, 2010. Sampling measures of tree diversity. For Ecol Manag 260: 1985-1996.

Pavoine S, Bonsall MB, 2011. Measuring biodiversity to explain community assembly: a unified approach. Biol Rev 86: 792-812.

Peres CA, 2000. Identifying keystone plant resources in tropical forests: The case of gums from Parkia pods. J Trop Ecol 16: 287-317.

Phillips OL, Vásquez Martínez R, Núñez Vargas P, Lorenzo Monteagudo A, Chuspe Zans ME, Galiano Sánchez W, Peña Cruz A, Timaná M, Yli-Halla M, Rose S, 2003. Efficient plot-based floristic assessment of tropical forests. J Trop Ecol 19: 629-645.

Pinheiro JC, Bates DM, 2000. Mixed-Effects Models in S and S-PLUS. 1st ed, Statistics and Computing. Springer-Verlag New York, New York, NY, USA. pp. 528.

Pinheiro JC, Bates DM, DebRoy S, Sarkar D, R Core Team, 2019. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-141.

Pitkänen S, 1998. The use of diversity indices to assess the diversity of vegetation in managed boreal forests. For Ecol Manag 112: 121-137.

Potts MD, Plotkin JB, Lee HS, Manokaran N, Ashton PS, Bossert WH, 2001. Sampling biodiversity: Effects of plot shape. Malays For 64: 29-34.

Schetter TA, Walters TL, Root KV, 2013. A multi-scale spatial analysis of native and exotic plant species richness within a mixed-disturbance oak savanna landscape. Environ Manage 52: 581-594.

Scheuber M, Köhl M, 2003. Assessment of non-wood-goods and services by cluster sampling. Advances in Forest Inventory for Sustainable Forest Management and Biodiversity Monitoring, Forestry Sciences; Köhl M, Marchetti M (eds). pp: 157-171. Springer Netherlands, Dordrecht, Netherlands.

Seidler TG, Plotkin JB, 2006. Seed dispersal and spatial pattern in tropical trees. PLOS Biol 4: 2132-2137.

Su S-H, Chang-Yang C-H, Lu C-L, Tsui C-C, Lin T-T, Lin C-L, Chiou W-L, Kuan L-H, Chen Z-S, Hsieh C-F, 2007. Fushan subtropical forest dynamics plot: tree species characteristics and distribution patterns. 1st ed, TFRI Extension Series. Taiwan Forestry Research Institute, Taipei, Taiwan.

Thompson SK, 2012. Sampling. 3rd ed, Wiley Series in Probability and Statistics. John Wiley & Sons Inc, Hoboken, NJ, USA. 436 pp.

Tokola T, Shrestha SM, 1999. Comparison of cluster-sampling techniques for forest inventory in southern Nepal. For Ecol Manag 116: 219-231.

Tomppo E, Gschwantner T, Lawrence M, McRoberts RE, 2010. National Forest Inventories: Pathways for Common Reporting. 1st ed. Springer Netherlands, Dordrecht, Netherlands. 612 pp.

Valentine HT, Ducey MJ, Gove JH, Lanz A, Affleck DLR, 2006. Corrections for cluster-plot slop. For Sci 52: 55-66.

West PW, 2013. Precision of inventory using different edge overlap methods. Can J For Res 43: 1081-1083.

Winter S, Chirici G, McRoberts RE, Hauk E, Tomppo E, 2008. Possibilities for harmonizing national forest inventory data for use in forest biodiversity assessments. Forestry 81: 33-44.

Yang T-R, Hsu Y-H, Kershaw JA, McGarrigle E, Kilham D, 2017. Big BAF sampling in mixed species forest structures of northeastern North America: influence of count and measure BAF under cost constraints. Forestry 90: 649-660.

Yang T-R, Kershaw JA, Weiskittel AR, Lam TY, McGarrigle E, 2019a. Influence of sample selection method and estimation technique on sample size requirements for wall-to-wall estimation of volume using airborne LiDAR. Forestry 92: 311-323.

Yang T-R, Lam TY, Su S-H, 2019b. A simulation study on the effects of plot size and shape on sampling plant species composition for biodiversity management. J Sustain For 38: 116-129.

Yim J-S, Shin M-Y, Son Y, Kleinn C, 2015. Cluster plot optimization for a large area forest resource inventory in Korea. For Sci Technol 11: 139-146.

How to Cite
QuonC., LamT.-Y., & LinH.-T. (2020). Designing Cluster Plots for Sampling Local Plant Species Composition for Biodiversity Management. Forest Systems, 29(1), e002.
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