Species-level tree crown maps improve predictions of tree recruit abundance in a tropical landscape - Barber, Cristina; Graves, Sarah; Hall, Jefferson; Zuidema, Pieter; Brandt, Jodi; Bohlman, Stephanie; Asner, Gregory; Bailón, Mario; Caughlin, T. Trevor (Dryad, 2021)
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).
Keywords: FOS: Agriculture, forestry, and fisheries