10.5061/DRYAD.Q5R472K
Tarbox, Bryan C.
University of Florida
Texas State University
Fiestas, Carlita
University of Florida
Caughlin, T. Trevor
Boise State University
Data from: Divergent rates of change between tree cover types in a
tropical pastoral region
Dryad
dataset
2019
Anthropocene
live fences
reforestation
silvopasture
forest and landscape restoration
Land Cover Change
trees outside forests
cadastral data
National Science Foundation
https://ror.org/021nxhr62
SBE-1415297
2019-09-10T00:00:00Z
2019-09-10T00:00:00Z
en
https://doi.org/10.1007/s10980-018-0730-0
2786118 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Context: Forest cover change analyses have revealed net forest gain in
many tropical regions. While most analyses have focused solely on forest
cover, trees outside forests are vital components of landscape integrity.
Quantifying regional-scale patterns of tree cover change, including
non-forest trees, could benefit forest and landscape restoration (FLR)
efforts. Objectives: We analyzed tree cover change in Southwestern Panama
to quantify: 1) patterns of change from 1998-2014, 2) differences in rates
of change between forest and non-forest classes, and 3) the relative
importance of social-ecological predictors of tree cover change between
classes. Methods: We digitized tree cover classes, including dispersed
trees, live fences, riparian forest, and forest, in very high resolution
images from 1998-2014. We then applied hurdle models to relate
social-ecological predictors to the probability and amount of tree cover
gain. Results: All tree cover classes increased in extent, but gains were
highly variable between classes. Non-forest tree cover accounted for 21%
of tree cover gains, while riparian trees constituted 31% of forest cover
gains. Drivers of tree cover change varied widely between classes, with
opposite impacts of some social-ecological predictors on non-forest and
forest cover. Conclusions: We demonstrate that key drivers of forest cover
change, including topography, road distance and historical forest cover,
do not explain rates of non-forest tree cover change. Consequently,
predictions from medium-resolution forest cover change analyses may not
apply to finer-scale patterns of tree cover. We highlight the opportunity
for FLR projects to target tree cover classes adapted to local social and
ecological conditions.
Digitized tree cover 1998This shapefile consists of polygons representing
tree cover in 150 x 150 m plots. Columns in the attribute table indicate
the year during which the aerial image used to digitize tree cover was
acquired (1998), the tree cover type (Dispersed, Fallow, Riparian, Fence,
and Forest), and the area of each polygon (in square
meters).digitized_polygons_1998.zipDigitized tree cover 2014This shapefile
consists of polygons representing tree cover in 150 x 150 m plots. Columns
in the attribute table indicate the year during which the aerial image
used to digitize tree cover was acquired (2014), the tree cover type
(Dispersed, Fallow, Riparian, Fence, and Forest), and the area of each
polygon (in square meters).digitized_polygons_2014.zipSampling squaresThis
shapefile represents sampling units for measuring tree cover. Each
sampling unit is a 150 x 150 m square. Squares were stratified to
properties using a cadastral data set, such that one square represents one
property. Within the sampling unit, all tree cover was digitized and
categorized into tree cover types. Areas without tree cover were not
digitized.USED_squares.zip
Los Santos Province
Panama
Azuero Peninsula
Latin America