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.

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 Byrsonima crassifolia, Calycophyllum candidissimum, Cedrela orodata, Guazuma ulmifolia, and Enterolobium cyclocarpum.

The data is a list that contains the following data:

recruits: 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 < 1 cm in diameter at breast height (DBH). We measured tree recruit abundance in transects of 100 m x 5 m, divided into 25 m² quadrats.

conspecifics_size: 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 m² 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).

hetersopecific_size: 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 m² 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).

property_ID: 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.

elevation: 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).