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Volume 32, Issue 5 e2585
ARTICLE

Species-level tree crown maps improve predictions of tree recruit abundance in a tropical landscape

Cristina Barber

Corresponding Author

Cristina Barber

Biological Sciences, Boise State University, Boise, Idaho, USA

Correspondence

Cristina Barber

Email: [email protected]

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Sarah J. Graves

Sarah J. Graves

Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, Wisconsin, USA

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Jefferson S. Hall

Jefferson S. Hall

Smithsonian Tropical Research Institute, ForestGEO, Panama City, Panama

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Pieter A. Zuidema

Pieter A. Zuidema

Forest Ecology and Forest Management group, Wageningen University, Wageningen, The Netherlands

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Jodi Brandt

Jodi Brandt

Human-Environment Systems, Boise State University, Boise, Idaho, USA

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Stephanie A. Bohlman

Stephanie A. Bohlman

School of Forest Resources and Conservation, University of Florida, Gainesville, Florida, USA

Smithsonian Tropical Research Institute, Panama City, Panama

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Gregory P. Asner

Gregory P. Asner

Center for Global Discovery and Conservation Science, Arizona State University, Tempe, Arizona, USA

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Mario Bailón

Mario Bailón

Smithsonian Tropical Research Institute, Panama City, Panama

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T. Trevor Caughlin

T. Trevor Caughlin

Biological Sciences, Boise State University, Boise, Idaho, USA

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First published: 25 March 2022
Citations: 1

Handling Editor: Xiangming Xiao.

Funding information: National Science Foundation, Grant/Award Number: 1415297; Smithsonian Tropical Research Institute (STRI)

Abstract

Predicting forest recovery at landscape scales will aid forest restoration efforts. The first step in successful forest recovery is tree recruitment. Forecasts of tree recruit abundance, derived from the landscape-scale distribution of seed sources (i.e., adult trees), could assist efforts to identify sites with high potential for natural regeneration. However, previous work revealed wide variation in the effect of seed sources on seedling abundance, from positive to no effect. We quantified the relationship between adult tree seed sources and tree recruits and predicted where natural recruitment would occur in a fragmented, tropical, agricultural landscape. We integrated species-specific tree crown maps generated from hyperspectral imagery and property ownership data with field data on the spatial distribution of tree recruits from five species. We then developed hierarchical Bayesian models to predict landscape-scale recruit abundance. Our models revealed that species-specific maps of tree crowns improved recruit abundance predictions. Conspecific crown area had a much stronger impact on recruitment abundance (8.00% increase in recruit abundance when conspecific tree density increases from zero to one tree; 95% credible interval (CI): 0.80% to 11.57%) than heterospecific crown area (0.03% increase with the addition of a single heterospecific tree, 95% CI: −0.60% to 0.68%). Individual property ownership was also an important predictor of recruit abundance: The best performing model had varying effects of conspecific and heterospecific crown area on recruit abundance, depending on individual property ownership. We demonstrate how novel remote sensing approaches and cadastral data can be used to generate high-resolution and landscape-level maps of tree recruit abundance. Spatial models parameterized with field, cadastral, and remote sensing data are poised to assist decision support for forest landscape restoration.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

DATA AVAILABILITY STATEMENT

Data (Barber et al., 2022) are available in the Dryad Digital Repository at https://doi.org/10.5061/dryad.dr7sqvb0d.