Document Type


Journal/Book Title/Conference

Methods in Ecology and Evolution

Author ORCID Identifier

Alice E. Stears:

Peter B. Adler:

Shannon E. Albeke:

Daniel C. Laughlin:






Wiley-Blackwell Publishing Ltd.

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First Page


Last Page


Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License


  1. Long-term demographic data are rare yet invaluable for conservation, management, and basic research on the underlying mechanisms of population and community dynamics. Historical and contemporary mapped datasets of plant location and basal area present a relatively untapped source of demographic records that, in some cases, span over 20 years of sequential data collection. However, these maps do not uniquely mark individual plants, making the process of collecting growth, survival, and recruitment data difficult.
  2. Recent efforts to translate historical maps of plant occurrence into shapefiles make it possible to use computer algorithms to track individuals through time and determine individual growth and survival. We summarize the plantTracker R package, which contains user-friendly functions to extract neighbourhood density, growth, and survival data from repeatedly-sampled maps of plant location and basal area. These functions can be used with data derived from quadrat maps, aerial photography, and remote sensing, and while designed for use with perennial plants, can be applied to any repeatedly mapped sessile organism.
  3. This package contains two primary functions: trackSpp(), which tracks individuals through time and assigns demographic data, as well as getNeighbors(), which calculates both within and between-species neighbourhood occupancy around each mapped individual. plantTracker also contains functions to estimate plot-level recruitment, calculate plot-level population growth rate, and create quadrat maps.
  4. We tested the accuracy of the trackSpp() function on two spatial demographic datasets. The function was nearly perfect at assigning individual identities and survival status when tested on maps of tree basal area and perennial forb point locations. In both cases, the function correctly assigned survival and recruitment with 99% accuracy. These accurate and precise functions will expand the amount of data available to investigate demographic processes, which are fundamental drivers of population, community, and ecosystem processes.

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