DOI: 10.5061/dryad.gxd2547xt
title: Tree mortality in an agricultural landscape of Southwestern Panama assessed using remote sensing and field data

description: Agricultural tree cover is declining globally, including the loss of
 large, scattered trees that function as keystone structures. Understanding
 the drivers of agricultural tree loss could help prevent further declines.
 However, the drivers of agricultural tree mortality vary across scales,
 from individual trees to landscapes, complicating efforts to quantify
 mortality risk. We applied high-resolution remote sensing and multi-method
 occupancy models to test hypotheses of drivers of tree mortality in a
 pastoral landscape of Southwestern Panama. Our approach enabled us to
 identify individual tree mortality across a &gt;20,000 ha area,
 encompassing a wide range of land use intensity. Neighboring tree cover
 was the strongest predictor of mortality, with a higher probability of
 death for isolated trees relative to trees with many neighbors.
 Landscape-level covariates also predicted mortality risk, including higher
 mortality closer to roads and in parcels with larger areas. These results
 implicate land use intensity as a primary driver of agricultural tree loss
 in our study area. At the individual tree level, we found that larger
 trees were more likely to die than smaller trees. Our study suggests that
 the trees with high ecosystem service value in a fragmented
 landscape—large, isolated trees—also face the highest mortality risk.
 Supporting agricultural practices that maintain trees in pastures is
 likely to decrease tree mortality in our study site, broadly
 representative of cattle ranching landscapes across Latin America. Our
 workflow could be implemented in other landscapes globally to prioritize
 agricultural tree conservation, paving the way for increased tree survival
 and improved ecosystem services.
description: The study was conducted in Southwestern Panama, covering a
 23,000-hectare area in Los Santos province. This region experiences a dry
 season from December to March, with most of its 1,700 mm of annual
 rainfall occurring between April and November. Historically, the landscape
 was dominated by dry tropical forests, but extensive cattle ranching has
 reduced forest cover to a small fraction of its original extent. The
 current landscape consists of active pastures, riparian corridors, and
 second-growth forests, with ongoing agricultural de-intensification and
 reforestation efforts. To assess tree mortality between
 2012 and 2019, data were integrated from three sources: field measurements
 of individual trees, aerial hyperspectral-lidar imagery, and
 high-resolution satellite imagery. Field data were collected during
 initial surveys in 2012 and 2013, when individual trees were identified,
 georeferenced, and classified by species. These trees were revisited in
 2019 to determine survival, with dead trees identified based on the
 presence of stumps or the complete absence of tree remains. Aerial
 hyperspectral-lidar imagery was collected in 2012, allowing for the
 segmentation of individual tree crowns across the landscape. In 2019,
 high-resolution satellite imagery was used to assess tree presence or
 absence by overlaying the 2012 tree crown segments onto the newer
 imagery. The field survey in 2012-2013 recorded 1,140
 tree crowns, identified to species using a GPS-enabled tablet. Five
 species were selected for analysis based on their classification accuracy
 in the hyperspectral imagery. In 2019, a subset of 269 trees from these
 species was revisited to confirm their status. A tree was classified as
 dead if no remains were found or if only a stump was present.
 Hyperspectral and lidar data from 2012 were used to generate a canopy
 height model and delineate tree crowns. A classification model applied to
 these data enabled species identification. From this dataset, a random
 selection of 10,000 tree crowns belonging to the five focal species was
 used for mortality assessment. Tree mortality was
 evaluated by comparing the 2012 tree crowns with the high-resolution
 satellite imagery from 2019. Researchers visually assessed whether tree
 crowns remained present or had disappeared. These assessments were
 conducted in Google Earth, with trained technicians manually reviewing
 tree locations from both time points. A validation process ensured
 consistency in classification decisions. Several
 covariates were included in the analysis to explore potential drivers of
 tree mortality. Tree size was quantified using measurements of tree height
 and crown area. Landscape features such as elevation, slope, and solar
 exposure were derived from a high-resolution digital elevation model.
 Distance to roads was measured as an indicator of accessibility and
 potential land-use pressure. Tree cover was calculated as the total area
 of neighboring tree crowns within a 30-meter radius of each focal tree.
 Additionally, trees were geolocated within land parcels using cadastral
 data to examine whether parcel size influenced mortality risk.
 A statistical model was used to estimate tree mortality while
 accounting for detection errors in the remotely sensed data. This model
 integrated field measurements with remotely sensed observations to improve
 mortality estimates. A Bayesian framework was used to quantify mortality
 probabilities, providing a robust approach for assessing the effects of
 tree size and landscape features on tree survival.
description: # Tree mortality in an agricultural landscape of Southwestern Panama
 assessed using remote sensing and field data
 [https://doi.org/10.5061/dryad.gxd2547xt](https://doi.org/10.5061/dryad.gxd2547xt) ## Description of the data and file structure These data represent field and remotely sensed measurements of tree mortality, along with landscape- and individual-level covariates for tree mortality. The file titled **"y.csv"** contains two columns. The first column represents the 269 trees whose mortality was measured directly in the field. The second column represents mortality estimated from remotely sensed data for 6,154 trees. Each row corresponds to a single tree. A value of 1 indicates tree survival, while a value of 0 indicates tree death. Values of -1 indicate trees that were measured using remotely sensed data but not field data. The file titled **"field.csv"** includes a single column with an index indicating which trees were measured in the field (out of 6,154 total trees). Similarly, the file titled **"remote.csv"** includes a single column with an index indicating which trees were measured only using remotely sensed data (out of 6,154 total trees). The file titled **"full_dataset.csv"** includes covariates, detection, and mortality data, with each row corresponding to a single tree. The R file, titled **"Run_Bestmodel.R,"** includes code to upload and process the .csv files and run the hierarchical Bayesian model for tree mortality. The Stan file, titled **"Bestmodel_Int_twolevelsofdetection_yhat.stan,"** contains the code for the mortality model, which accounts for imperfect detection. ### Files and variables **File: y.csv** **Description:** This file contains tree mortality data, including field-measured and remotely sensed estimates. **Variables:** * **y_1:** Mortality data for 269 trees measured directly in the field (1 = survival, 0 = death). * **y_2:** Mortality data estimated from remotely sensed data for 6,154 trees (1 = survival, 0 = death, -1 = trees measured only with remote sensing and not field data). #### File: field.csv **Description:** An index for which trees were measured with field data (out of 6,154 total trees). ##### Variables * x, an index #### File: remote.csv **Description:** An index for which trees were measured only using remotely sensed data (out of 6,154 total trees). ##### Variables * x, an index #### File: full\_dataset.csv **Description:** This file contains tree-level covariates related to mortality.  **Variables:** * **sp:** An index indicating which of the five species the tree belongs to. * **height:** The average height of each tree in meters. * **ca:** The crown area of each tree in square meters. * **siteExp:** An index that combines slope and aspect to estimate exposure for each tree. This variable is unitless. * **elev:** The elevation of each tree in meters. * **propid:** An index indicating which property each tree belongs to. This property index was created randomly for the purpose of this study and cannot be linked to any identifying information on property identity. * **fc2012:** The summed area of neighboring tree cover within a 30 m radius around each tree in square meters. * **roadP:** The distance from each tree to the nearest road in meters. * **propSize:** The area of each property in hectares. ## Code/software Two open source software packages are required to run the code. R version 4.4.1 (2024-06-14 ucrt) and Stan version "2.32.2". The only package required to run the code is the "rstan" package. The R file, titled **"Run_Bestmodel.R,"** includes code to upload and process the .csv files and run the hierarchical Bayesian model for tree mortality. The Stan file, titled **"Bestmodel_Int_twolevelsofdetection_yhat.stan,"** contains the code for the mortality model, which accounts for imperfectly detected mortality. Using the rstan package, the entire model can be run from R. ## Access information Other publicly accessible locations of the data: * None Data was derived from the following sources: * Road network data was downloaded from the Smithsonian Tropical Research Institution’s GIS Data\ Portal ([https://stridata-si.opendata.arcgis.com/](https://stridata-si.opendata.arcgis.com/)). * Parcel data, representing landowner properties, is from Panama's National Authority for the Administration of Land. * Elevation, solar exposure, tree species identity, neighborhood crown area, and tree height are derived from aerial lidar and hyperspectral data, acquired by the Global Airborne Observatory in 2012.