DOI: 10.5061/dryad.dr7sqvb0d
title: Species-level tree crown maps improve predictions of tree recruit abundance in a tropical landscape

description: 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 has 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 boundaries data on
 individual property ownership 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% 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.
description: This
 database was used to create predictive models for natural regeneration at
 the landscape scale. It includes data on the recruit abundance of five
 species, the total crown size of the conspecific and heterospecific trees
 in a radius of 100 m from the recruits, elevation, and individual property
 identity where the recruits were growing. The five species include
 <em>Byrsonima crassifolia, </em><em>Calycophyllum candidissimum</em><em>, Cedrela orodata, Guazuma ulmifolia</em>, and <em>Enterolobium cyclocarpum.</em> The data is a list that contains the following data: <strong>recruits:</strong> This is the tree recruit abundance in July 2018. We measured it by counting individuals of our focal species in transects. We defined tree recruits as individuals at least 0.5 m in height but &lt; 1 cm in diameter at breast height (DBH). We measured tree recruit abundance in transects of 100 m x 5 m, divided into 25 m<sup>2</sup> quadrats. <strong>conspecifics_size:</strong> We separated the conspecific crown area for focal species, representing tree crowns of the same species as recruits. We then summed the tree crown area within 100 m of the center of each 25 m<sup>2</sup> quadrat. We used a map of adult tree species derived from aerial lidar and hyperspectral data to obtain the data (Graves et al. 2016). These aerial data were collected by the Global Airborne Observatory (GAO; formerly the Carnegie Airborne Observatory) in January 2012 (Asner et al. 2012). <strong>hetersopecific_size:</strong> We separated the heterospecific crown area for each focal species, representing tree crowns of the same species as recruits. We then summed the tree crown area within 100 m of the center of each 25 m<sup>2</sup> quadrat. We used a map of adult tree species derived from aerial lidar and hyperspectral data to obtain the data (Graves et al. 2016). These aerial data were collected by the Global Airborne Observatory (GAO; formerly the Carnegie Airborne Observatory) in January 2012 (Asner et al. 2012). <strong>property_ID:</strong> A number indicating the identity of the parcel in which recruit abundance was measured. To obtain the data, we used a cadastral dataset developed by Panama's National Authority for the Administration of Lands and provided by the Fundación Pro Eco Azuero. <strong>elevation:</strong> We extracted the recruits' elevation using a digital elevation model with 1.13 m spatial resolution, developed from the aerial lidar over our study area (Asner et al. 2012).