10.5061/DRYAD.8GTHT76KV
Guevara Andino, Juan Ernesto
0000-0002-5433-6218
Field Museum of Natural History
Pitman, Nigel C.A.
Field Museum of Natural History
ter Steege, Hans
Naturalis Biodiversity Center
Peralvo, Manuel
Consortium for Sustainable Development of the Andean Ecoregion
Cerón, Carlos
Universidad Central
Fine, Paul V.A.
University of California, Berkeley
The contribution of environmental and dispersal filters on beta diversity
patterns in Amazonian tree communities
Dryad
dataset
2020
American Philosophical Society
https://ror.org/04egvf158
Lewis and Clark Grant for Exploration
Garden Club of America
https://ror.org/03s9h8898
Tropical Botany Grant
University of California, Berkeley
https://ror.org/01an7q238
Summer Research Grant
2021-09-13T00:00:00Z
2021-09-13T00:00:00Z
en
https://doi.org/10.1002/ecy.2894
158226 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Environmental filters (e.g. climate, geomorphology and soils) and
dispersal filters are key determinants of species distributions of
Amazonian tree communities. However, a comprehensive analysis of the role
of environmental and dispersal filters is needed to understand the
ecological and evolutionary processes that drive phylogenetic and
taxonomic turnover of Amazonian tree communities. We compare measures of
taxonomic and phylogenetic beta diversity in 40 one-hectare plots to test
the relative importance of climate, soils, geology, geomorphology, pure
spatial variables and the spatial variation of environmental drivers of
phylogenetic and taxonomic turnover in Ecuadorian Amazon tree communities.
We found low phylogenetic and high taxonomic turnover with respect to
environmental and dispersal filters. In addition, our results suggest that
climate is a significantly better predictor of phylogenetic turnover and
species turnover than geomorphology and soils at all spatial scales. The
influence of climate as a predictor of phylogenetic turnover was stronger
at broader spatial scales (50 km2) whereas geomorphology and soils appear
to be better predictors of taxonomic turnover at mid (5 km2) and fine
spatial scales (0.5 km2) but a weak predictor of phylogenetic turnover at
broad spatial scales. We also found that the combined effect of
geomorphology and soils was significantly higher for species turnover at
all spatial scales but not for phylogenetic turnover at large spatial
scales. Geographic distances as proxy of dispersal limitation was a better
predictor of phylogenetic turnover at distances of 50<500 km. Our
findings suggest that climatic variation at local and regional scales can
better predict phylogenetic and taxonomic turnover than geomorphology and
soils.
The 40 one hectare plot work used in this study are part of the Amazon
Tree diversity Network and have been established by the lead author and
co-authors in the past 20 years representing an unprecedented data set. In
the past four years, we established 15 one-hectare plots in both terra
firme and white sand forests in the Ecuadorian Amazon. We established 8
plots on alluvial terraces of the Aguarico River towards the north of the
Ecuadorian Amazon; the age of these units ranges from Pliocene to
Pleistocene origin (http://www.geoinvestigacion.gob..ec, Laraque et
al.2009) The landscape is mostly characterized by large areas that
correspond to Pleistocene alluvial terraces that occasionally suffer
flooding events. These geomorphological units are interrupted only by high
terraces with flat surfaces that have not suffered erosion of their
surfaces (Saunders 2008; Wesselingh et al. 2006). Two additional plots
were established in old alluvial terraces of Napo River, these units as
well as the units located in the Aguarico River are high terraces that
presumably constitute old flood plains of the previously mentioned rivers
(Saunders 2008). Ten plots were established in the Pastaza megafan which
is a massive alluvial deposit located in the southwestern Ecuadorian
Amazon, evidence suggests that the modern megafan complex dates from the
Pliocene-Pleistocene and recent alluvial processes have occurred between
the last 180 000-30 000 yrs. (Rasanen et al. 1995; Bernal et al. 2011).
Five plots were established in areas belonging to plateaus originated
during the Cretaceous period; these geomorphological units are located in
the lowest part of Cordillera del Condor below 500 m. The remaining 25 one
hectare plots were established in the Yasuní national Park and the
Tigre-Corrientes watershed. The landscape in both areas is characterized
by the predominance of geomorphological units such as highly dissected
hills occasionally interrupted by valleys (Pitman 2000). The landscape is
dominated by Curaray and Chambira formations from Miocene and Mio-Pliocene
origin respectively and soils are characterized by higher nutrients
content (Pitman et al. 2008). Geomorphological variables and proximity
analysis Four geographic variables (hierarchical slope position, slope,
dem and landsat) were used in the analysis describing the geomorphology
and land cover features in the vicinity of the forest plots. Digital
terrain elevation data for the Ecuadorian Amazon was obtained from the
Shuttle Radar Topography Mission (SRTM) distributed by the USGS through
the Earth Explorer platform (https://earthexplorer.usgs.gov/). The SRTM
dataset has worldwide coverage of void filled elevation data at a
resolution of 1 arc-second (30 meters). Topographic slope in degrees was
calculated from the elevation data using the Spatial Analyst extension in
ArcGIS 10.3 software from ESRI (Environmental Systems Resource Institute).
Hierarchical Slope Position identifies topographic exposure (ridge, slope,
valley bottom, etc) by applying moving windows with increasing radii to a
digital elevation model (DEM) (Murphy et al. 2010). The exposure is a
ridge if the elevation of the center cell in the window is higher than the
average of the cells in the window. The opposite case corresponds to a
valley bottom or toe slope. Hierarchical integration is done by starting
with exposure values for the largest (user defined) window and adding
values from smaller windows if their absolute standardized values exceed
the values of the larger scale map. The variable was calculated using the
Geomorphometry and Gradient Metrics (version 2.0) for ArcGIS (Evans et al.
2014) using windows of radii between 2 and 10 pixels with increments of
two pixels. Slope position was measured by subtracting the Slope Position
average neighbor values from a focal value. Positive values indicate that
the central point is located higher than its average surroundings, while
negative indicates a position lower than the average. The range of the
metric depends not only on differences but also on the defined
neighborhood. This metric is also referred to as Topographic Position
Index (TPI) (Guisan et al. 1999). Digital elevation models (DEM) measures
the bare-earth surface based on raster grids of the elevation between two
or more points. We obtained data for Ecuadorian Amazon from the SRTM
90-meter resolution Digital Elevation Model developed by the NASA. We
concatenated the different mosaics of topography in ArcMap 10.5.1 using
the Spatial Statistics tool. Land cover information was obtained from a
mosaic of Landsat images for the period 2010-2014 that had been created
for the Ministry of the Environment of Ecuador. The mosaic was was created
using the approach described in Hansen et al. (2013) that includes (i)
image resampling, (ii) conversion of raw digital values (DN) to top of
atmosphere (TOA) reflectance, (iii) cloud/shadow/water screening and
quality assessment (QA), and (iv) image normalization. A principal
components analysis was performed using the three RGB bands of the mosaic
and the first component, that explained 96.4% of the variance, was used
for further analysis. For each plot, a set of circular buffers with areas
of 0.5 km2, 5 km2 and 50-100 km2. Descriptive statistics were calculated
for each variable at each scale defined by the buffer areas using zonal
statistics tools in ArcGIS. Climatic variables In order to assess the role
of climatic variables in the patterns of taxonomic and phylogenetic
turnover we used 19 climatic variables from Bioclim at 30 seconds of
resolution as an initial set of variables.