David MacFarlane

David MacFarlane

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Professor, Forest Measurements and Modeling
Department of Forestry



PhD in Ecology and Evolution, Rutgers University, 2001
BS in Natural Resource Management, Cook College, 1995
LTE (Licensed Tree Expert), 1997

Research Interests

My research program focuses on answering two basic questions:

  1. How do we know what we think we know about forests? (i.e., forest measurements)
  2. How can we utilize what we know to better understand how forests work and how to manage them more effectively? (i.e., forest modeling).

Forests are complex ecosystems that are intricately linked to environmental quality and human industry. I am interested in developing new methods for measuring attributes of forests, particularly those attributes that are typically given less attention (under-measured), such as tree bark & branch measurements. I am also interested in improving methods for accurate forest resource inventories, particularly wood products and carbon biomass inventories. This involves developing novel survey and sampling methodologies as well as new measurements. An exciting area of research I am working in now involves using models to improve sampling methods.


Hassan C David, D.W. MacFarlane, Sylvio Péllico Netto, Ana Paula Dalla Corte, Daniel Piotto, Yeda M M de Oliveira, Vinicius A Morais, Carlos R Sanquetta, Rorai P M Neto. 2019. Exploring coarse- to fine-scale approaches for mapping and estimating forest volume from Brazilian National Forest Inventory data, Forestry: An International Journal of Forest Research, , cpz030, https://doi.org/10.1093/forestry/cpz030

Neil R Ver Planck and MacFarlane, D.W. 2019. Branch mass allocation increases wind throw risk for Fagus grandifolia, Forestry: An International Journal of Forest Research, , cpz001, https://doi.org/10.1093/forestry/cpz001

Dettman, G.T. and MacFarlane, D.W. 2018. Trans-species predictors of tree leaf mass. Ecological Applications, 29(1): e01817. 10.1002/eap.1817

Clough, B.J., Domke, G.M., MacFarlane, D.W., Radtke, P.J., Russell, M.B., Weiskittel, A.R. 2018. Comparison of approaches for predicting total tree aboveground biomass and its components in the primary conifer and hardwood species of eastern United States. Forestry: An International Journal of Forest Research, Forestry; 00, 1–14, doi:10.1093/forestry/cpy016.

Frank, J., Castle, M., Westfall, J., Weiskittel, A., MacFarlane, D.W., Baral, S, Radtke, P., and Pelletier, G.  2018. Variation in occurrence and extent of internal stem decay in standing trees across eastern US and Canada: Evaluation of alternative modeling approaches and influential factors. Forestry: An International Journal of Forest Research; 91, 382–399, doi:10.1093/forestry/cpx054

David, H.C., Gomes de Araújo, E.J., Morais, V.A., Scolforo, J.R.S., Marques, J.M., Péllico, S. and MacFarlane, D.W. 2017. Carbon stock classification for tropical forests in Brazil: Understanding the effect of stand and climate variables. Forest Ecology and Management, 404: 241–250.

McCann, R.S., Messina, J.P., MacFarlane, D.W.,, M. Nabie Bayoh, Gimnig, J.E. Giorgi, E. and Walker, E.D. 2017.  Explaining variation in adult Anopheles indoor resting abundance: the relative effects of larval habitat proximity and insecticide-treated bed net use. Malaria Journal, 16:288. DOI 10.1186/s12936-017-1938-1.

MacFarlane, D.W. and Kane, B., 2017. Neighbour effects on tree architecture: functional trade‐offs balancing crown competitiveness with wind resistance. Functional Ecology, 31(8), pp.1624-1636.

Radtke, P., Walker, D., Frank, J., Weiskittel, A., DeYoung, C., MacFarlane, D.W., Domke, G., Woodall, C., Coulston, J. and Westfall, J., 2017. Improved accuracy of aboveground biomass and carbon estimates for live trees in forests of the eastern United States. Forestry: An International Journal of Forest Research, 90(1), pp. 32-46.

MacFarlane, D.W., Weiskittel, A.R. 2016. A new method for capturing stem taper variation for trees of diverse morphological types. Can. J. For. Res. 46: 804–815.

MacFarlane, D.W., Kinzer, A.T., Banks, J.E. 2015. Coupled human-natural regeneration of indigenous coastal dry forest in Kenya. Forest Ecology and Management, 354: 149–159.

MacFarlane, D.W. 2015. A generalized tree component biomass model derived from principles of variable allometry. Forest Ecology and Management, 354: 43-55.

Weiskittel, A.R., MacFarlane, D.W., Radtke, P.J. Affleck, D.L.R., Hailemariam, T.,Westfall, J.A., Woodall, C.W., and Coulston, J.W. 2015. A call to improve methods for estimating tree biomass for regional and national assessments. Journal of Forestry, 113(4):414–424.

Ver Planck N.R., MacFarlane, D.W. 2015. A vertically integrated whole-tree biomass Model. Trees, 29: 449–460.

McCann, R.S., Messina, J.P., MacFarlane, D.W., Nabie Bayoh, B.M., Vulule, J.M., Gimnig, J.E. and Walker, E.D. 2014.  Modeling larval malaria vector habitat locations using landscape features and cumulative precipitation measure. International Journal of Health Geographics, 13:17, 1-12.

MacFarlane, D.W., Kuyah, S., Mulia, R., Dietz, J., Muthuri, C., and Van Noordwijk, M.  2014. Evaluating a non-destructive method for calibrating tree biomass equations derived from tree branching architecture.  Trees, 28: 807–817.

Ver Planck, N.R. and MacFarlane, D.W., 2014. Modelling vertical allocation of tree stem and branch volume for hardwoods. Forestry: An International Journal of Forest Research, 87(3), pp.459-469.

An, Hong Su, MacFarlane, David W. 2012. Comparing a new model-based method to fixed-area sampling for estimating the abundance of standing dead trees. Forestry: An International Journal of Forest Research, 2013; 86, 231–239.

Wang, Z., MacFarlane, D.W. 2012. Evaluating the biomass production of coppiced willow and poplar clones in Michigan, USA, over multiple rotations and different growing conditions. Biomass and Bioenergy, 46, 380-388.

Woodall, C.W., Domke, G.M., MacFarlane, D.W., and Oswalt, C.M.  2011.  Comparing field- and model-based standing dead tree carbon stock estimates across forests of the US.  Forestry: An International Journal of Forest Research; 85: 125–133.

MacFarlane, D.W. 2011. Allometric scaling of branch volume in hardwood trees in Michigan, USA: implications for improvements in above-ground forest carbon biomass inventories. For. Sci. 57(6):451– 459.

Finley, A.O., S. Banerjee, and MacFarlane, D.W.. 2011. A Hierarchical Model for Quantifying Forest Variables over Large Heterogeneous Landscapes with Uncertain Forest Areas. Journal of the American Statistical Association. March 2011, Vol. 106, No. 493, 31-48.

MacFarlane, D.W.  2010. Predicting branch to bole volume scaling relationships from varying centroids of tree bole volume. Can. J. For. Res. 40(12): 2278–2289.

MacFarlane, D.W. and Luo, A.  2009.  Quantifying tree and forest bark structure with a bark-fissure index. Can. J. For. Res. 39(10): 1859–1870.

MacFarlane, D.W.  2009. Potential availability of urban wood biomass in Michigan:  implications for energy production, carbon sequestration and sustainable forest management in the USA. Biomass & Bioenergy, 33, 628-634.

Rubin B.D. and MacFarlane, D.W.  2008.  Using the space-time permutation scan statistic to map anomalous diameter distributions drawn from landscape-scale forest inventories. For. Sci.  54(5): 523-533.

MacFarlane, D.W.  2007.  Quantifying urban saw timber abundance and quality in south eastern Lower Michigan, U.S.  Arboriculture and Urban Forestry 33(4): 253-263.

Zakrzewski, W.T., MacFarlane D.W.  2006. Regional stem profile model for cross border comparisons of harvested red pine (Pinus resinosa Ait.) in Ontario and Michigan, For. Sci. 52(4): 468-475.

MacFarlane, D.W. and Kobe, R.K. 2006. Selecting models for capturing tree size effects on growth-resource relationships.  Can. J. For. Res. 36: 1695-1704.

MacFarlane, D.W. and Meyer, S.P.  2005.  Characteristics and distribution of potential ash tree hosts for Emerald Ash Borer. For. Ecol. &  Manage.,  213: 15-24.

MacFarlane, D.W.  2004. Ecologically stratified height-diameter models for hardwood species in northwestern Lower Michigan.  In Proceedings of the 14th Central Hardwoods Forest Conference, GTR-NE-316, Wooster, Ohio, March 17-19th, 2004.

MacFarlane, D.W., Green, E.J., Brunner, A., and Amateis, R.L.  2003.  Modeling loblolly pine canopy dynamics for a light capture model.  For. Ecol. &  Manage.  173: 145-168.

MacFarlane, D.W., Green, E.J., Brunner, A., and Burkhart, H.E.  2002. Predicting survival and growth rates for individual loblolly pine trees from light capture estimates.  Can. J. For. Res. 32(11): 1970-1983.

MacFarlane, D.W., Green, E.J., and Burkhart, H.E. 2000.  Population density influences assessment and application of site index.  Can. J. For. Res. 30(9): 1472-1475.

MacFarlane, D.W., Green, E.J., and Valentine, H.T. 2000.  Incorporating uncertainty into the parameters of a forest growth model.  Ecol. Model. 134:27-40.

Green, E.J., MacFarlane, D.W., and Valentine, H.T. 2000. Bayesian synthesis for quantifying uncertainty in predictions from process models. Tree physiology 20: 415-419

Green, E.J., MacFarlane, D.W., Valentine, H.T. and Strawderman, W.E.  1999.  Assessing uncertainty in a stand growth model by Bayesian Synthesis.  For. Sci. 45(4): 528-538

Valentine, H.T., Amateis, R.L., Burkhart, H.E., Gregoire, T.G., Hollinger, D.Y. and MacFarlane, D.W.  1999.  Growth of loblolly pine in a changing atmosphere.  South. J. Appl. For. 23(4):212-216.