Animal burrowing activity affects soil texture, bulk density, soil water
content, and redistribution of nutrients. All of these parameters in turn
influence sediment redistribution, which shapes the earth's surface. Hence it
is important to include bioturbation into hillslope sediment transport
models. However, the inclusion of burrowing animals into hillslope-wide
models has thus far been limited and has largely omitted vertebrate
bioturbators, which can be major agents of bioturbation, especially in drier
areas.
Here, we included vertebrate bioturbator burrows into a semi-empirical
Morgan–Morgan–Finney soil erosion model to allow a general approach to
the assessment of the impacts of bioturbation on sediment redistribution within four
sites along the Chilean climate gradient. For this, we predicted the
distribution of burrows by applying machine learning techniques in
combination with remotely sensed data in the hillslope catchment. Then, we
adjusted the spatial model parameters at predicted burrow locations based on
field and laboratory measurements. We validated the model using field
sediment fences. We estimated the impact of bioturbator burrows on surface
processes. Lastly, we analyzed how the impact of bioturbation on sediment
redistribution depends on the burrow structure, climate, topography, and
adjacent vegetation.
Including bioturbation greatly increased model performance and demonstrates
the overall importance of vertebrate bioturbators in enhancing both sediment
erosion and accumulation along hillslopes, though this impact is clearly
staggered according to climatic conditions. Burrowing vertebrates increased
sediment accumulation by 137.8 % ± 16.4 % in the arid zone (3.53 kg ha-1 yr-1 vs. 48.79 kg ha-1 yr-1), sediment
erosion by 6.5 % ± 0.7 % in the semi-arid zone (129.16 kg ha-1 yr-1 vs. 122.05 kg ha-1 yr-1), and sediment
erosion by 15.6 % ± 0.3 % in the Mediterranean zone (4602.69 kg ha-1 yr-1 vs. 3980.96 kg ha-1 yr-1).
Bioturbating animals seem to play only a negligible role in the humid zone.
Within all climate zones, bioturbation did not uniformly increase erosion or
accumulation within the whole hillslope catchment. This depended on
adjusting environmental parameters. Bioturbation increased erosion with
increasing slope, sink connectivity, and topography ruggedness and decreasing
vegetation cover and soil wetness. Bioturbation increased sediment
accumulation with increasing surface roughness, soil wetness, and vegetation
cover.
Deutsche ForschungsgemeinschaftBE1780/52-1, LA3521/1-1, FA 925/12-1, BR 1293-18-1Introduction
Bioturbation was shown to shape the land surface (Hazelhoff et al., 1981;
Istanbulluoglu, 2005; Taylor et al., 2019; Tucker and Hancock, 2010;
Whitesides and Butler, 2016; Wilkinson et al., 2009; Corenblit et al., 2021)
by influencing surface microtopography (Reichman and Seabloom, 2002; Kinlaw
and Grasmueck, 2012; Debruyn and Conacher, 1994) and soil properties such
as soil porosity, permeability, and infiltration (Reichman and Seabloom,
2002; Yair, 1995; Hancock and Lowry, 2021; Ridd, 1996; Hall et al., 1999;
Coombes, 2016; Larsen et al., 2021). Cumulatively, these modifications lead
to changes in sediment redistribution (Gabet et al., 2003; Nkem et al.,
2000; Wilkinson et al., 2009) and hence have the potential to affect surface
topography and nutrient redistribution on large spatial and temporal scales.
To quantify these effects, the shared role of climate, landscape
characteristics, and burrowing dynamics on sediment redistribution needs to
be understood.
On a local scale, currently used field methods to monitor sediment
redistribution under real-life conditions are mainly erosion pins, splash
boards, and rainfall simulators (Imeson and Kwaad, 1976; Wei et al., 2007; Le
Hir et al., 2007; G. Li et al., 2019; T. C. Li et al., 2019; T. Li et al., 2018;
Voiculescu et al., 2019; Chen et al., 2021; Übernickel et al., 2021a).
The monitoring of box experiments yields a high spatiotemporal resolution
and can also be linked to mathematical equations, such as random walks
(Boudreau, 1986; Wheatcroft et al., 1990), stochastic differential equations
(Boudreau, 1989; Milstead et al., 2007), finite-difference mass balancing
(Soetaert et al., 1996; François et al., 1997), and Markov chain theory
(Jumars et al., 1981; Foster, 1985; Trauth, 1998; Shull, 2001) to describe
sediment redistribution.
Previously used methods have, however, several limitations when studying
bioturbation. Field measurements likely lead to an underestimation of
sediment fluxes, as they are one-time or seasonal measurements and thus do
not capture the continuous excavation of the sediment by the animal
(Grigusova et al., 2022) at a high temporal resolution. Box experiments and
the mathematical equations derived from them describe bioturbation as an
isolated process and ignore adjacent environmental parameters (such as
climate or vegetation). However, the field measurements showed both
positive (Hazelhoff et al., 1981; Black and Montgomery, 1991; Chen et al.,
2021) and negative impact of bioturbation on erosion (Imeson and Kwaad,
1976; Hakonson, 1999). Also, previous field-based studies observed an
increased bioturbation activity with higher (Milstead et al., 2007; Meserve,
1981; Tews et al., 2004; Wu et al., 2021; Ferro and Barquez, 2009) and
lower vegetation cover (Simonetti, 1989; S. Zhang et al., 2020; Q. Zhang et
al., 2019; Qin et al., 2021). Furthermore, soil mixing rates are not
homogenous throughout the year; they depend on the animal phenological cycles
(Eccard and Herde, 2013; Jimenez et al., 1992; Katzman et al., 2018;
Malizia, 1998; Morgan and Duzant, 2008; Monteverde and Piudo, 2011; Gray et
al., 2020; Yu et al., 2017).
Another approach offers raster-based soil erosion and landscape evolution
models which integrate co-dependencies between bioturbation-relevant
environmental parameters (Black and Montgomery, 1991; Meysman et al., 2003;
Yoo et al., 2005; Schiffers et al., 2011). The most common soil erosion models
are empirical (Wischmeier and Smith, 1978; Williams, 1975; Renard et al.,
1991), process-based (Morgan et al., 1998; Roo et al., 1996; Nearing et al.,
1989; Beasley et al., 1980), and semi-empirical models, the latter of which
are a combination of both (Morgan et al., 1984; Beven and Kirkby, 1979).
Process-based models are based on a mechanistic understanding of the
underlying physical, chemical, and biological processes that govern the
behavior of the system being studied. They must be parameterized for each
site; however, these models explicitly represent the governing equations and
simulate the system's behavior by numerically solving these equations.
Process-based models are generally considered to be more realistic and
accurate than empirical models because they capture the fundamental
processes that drive the system's behavior. However, process-based models
can be computationally expensive, require more data and knowledge of system
properties, and may require complex numerical algorithms (Morgan et al.,
1998; Roo et al., 1996; Nearing et al., 1989; Beasley et al., 1980).
Within empirical models, on the other hand, the physical equations are
completely replaced by empirically determined equations which only hold for
the specific area they are derived for. These models are generally simpler,
are less computationally expensive, and require more data and knowledge of
system properties than process-based models. However, empirical models also
tend to be less accurate than process-based models, particularly when
applying beyond the range of data used to fit the model. In contrast to
physical-based models, empirical models may not be applicable to new or
different conditions, as they are based on observed relationships and do not
capture the underlying processes that govern system behavior (Wischmeier
and Smith, 1978; Williams, 1975; Renard et al., 1991).
Semi-empirical models combine the advantages of the both model types (Morgan
et al., 1984; Morgan, 2001; Morgan and Duzant, 2008; Devia et al., 2015;
Lilhare et al., 2015). Most landscape models have not yet implemented the
impacts of bioturbators on water and sediment fluxes (Brosens et al., 2020;
Anderson et al., 2019; Braun et al., 2016; Cohen et al., 2010, 2015; Carretier et al., 2014; Welivitiya et al., 2019). There are numerous
models describing benthic soil mixing (Francois et al., 1997, 2002; Kadko and Heath 1984; Croix et al., 2002), biodiffusion caused by all
invertebrate bioturbators (Meysman et al., 2005; Rakotomalala et al., 2015;
Morris et al., 2006), and vertical soil mixing and lateral sediment
redistribution caused by single invertebrate species (Orvain et al., 2006;
Román-Sánchez et al., 2019; Orvain, 2003, 2005; Sanford,
2008). However, there are also models which described the impact of
bioturbation on sediment redistribution by vertebrate animal species,
such as the impact of pocket gophers on non-linear hillslope diffusion
(Gabet, 2000) or on the creation of Mima mounds (Gabet et al., 2014). Several
models include soil vertical mixing caused by bioturbation and its effect on
landscape evolution on a millennial scale. This rather large spatiotemporal
scale, however, means an omission of the natural variability in burrow sizes
and densities, climate zones, and seasonality. In these models, soil erosion
increases proportionally with increasing bioturbation, vertical soil
mixing rates are uniform, and bioturbation is positively linked with
vegetation cover (Temme and Vanwalleghem, 2016; Vanwalleghem et al., 2013;
Yoo and Mudd, 2008; Pelletier et al., 2013). None of the previous studies
included vertebrate bioturbator burrows of various sizes and spatial
distribution by adjusting the soil properties and topography into a
raster-based area-wide soil erosion model. This approach would enable us to
understand the impact of all vertebrate bioturbators by considering the spatial
distribution and variable impacts of bioturbator burrows on sediment
redistribution. For this, bioturbation has to be included into erosion
models at a spatial resolution which allows the imitation of the surface processes
occurring within and near the burrow and at a temporal resolution which
captures the animal daily burrowing behavior.
A suitable model which can be extended to include continuous bioturbating
activity is the semi-empirical Morgan–Morgan–Finney soil erosion model
(Morgan et al., 1984; Morgan, 2001). This model was successfully tested in
several climate zones and land use types, such as Mediterranean sites (Jong
et al., 1999); rainfed agrosystems, fields, and pastures (López-Vicente
et al., 2008); the East African Highlands (Vigiak et al., 2005); and humid forests
(Vieira et al., 2014). One of the recently developed improvements of this
model is the daily Morgan–Morgan–Finney model (DMMF), which introduces
subsurface flow and vegetation structures (type, size, height, root depth) and
enables modeling at a high spatial (0.5 m) and temporal (daily) resolution
(Choi et al., 2017). These improvements yield the potential to integrate the
bioturbation into the model, as the burrowing activity is not constant and
depends on vegetation structure (Tews et al., 2004; Ferro and Barquez,
2009).
In this study, we include vertebrate bioturbator burrows into a
semi-empirical soil erosion model (DMMF) at a daily temporal and 0.5 m
spatial resolution. For this, we predict the distribution of burrows by
applying machine learning techniques in combination with using remotely
sensed data as predictors. Then, we adjust soil properties, topography, and
vegetation properties at predicted burrow locations based on field and
laboratory measurements. We validate the model using field sediment fences.
We run the model for a time period of 6 years, once with and without burrow
adjustments. We estimate the impact of bioturbator burrows on sediment
redistribution (including accumulation, erosion, and excavation) and
surface runoff within four sites along the Chilean climate gradient. Lastly,
we analyze how the impact of bioturbation on sediment redistribution depends
on the burrow structure, climate, topography, and adjacent vegetation. Our
study shows the importance of including bioturbation into erosion modeling
and describes the interplay between bioturbation, environmental parameters
such vegetation and topography, and sediment redistribution.
Study area
Our study was performed along a climate and ecological gradient in Chile
(Übernickel et al., 2021b), comprising four study sites in the Chilean
Coastal Cordillera: Pan de Azúcar (PdA) National Park (NP), Santa Gracia
(SG), La Campana (LC) NP, and Nahuelbuta (NA) NP (Fig. 1). PdA NP is located
in the arid zone in a fog-laden environment in the southern part of the
Atacama Desert, with almost no rainfall. The vegetation cover is less than 5 % and dominated by small desert shrubs, several types of cacti, and
biocrusts (Lehnert et al., 2018). SG is a natural reserve located in the
semi-arid zone near La Serena, which is dominated by goat grazing. The
vegetation consists of shrubs and cacti, covering up to 40 % of the study
area. LC NP is part of the Mediterranean-type climate zone in the Valparaíso
Region and is also affected by cattle. The study site is dominated by an
evergreen sclerophyllous forest with endemic palms. The canopy reaches a
height of up to 9 m, and the understory consists of deciduous shrubs and
herbs. NA is located in the humid–temperate zone and characterized by a
dense evergreen Araucaria forest comprising broadleaved trees with heights of up to
14 m. The ground is covered by bamboo, shrubs, and herbs (Bernhard et al.,
2018; Oeser et al., 2018). The most common bioturbating vertebrate animal
species recorded within these sites are carnivores of the family Canidae
(Lycalopex culpaeus, Lycalopex griseus) as well as rodents of the families Abrocomidae (Abrocoma bennetti), Chinchillidae
(Lagidium viscacia), Cricetidae (Abrothrix andinus, Phyllotis xanthopygus, Phyllotis limatus, Phyllotis darwini), and Octodontidae (Cerqueira, 1985; Jimenez et al., 1992;
Übernickel et al., 2021a).
Study area and study sites. Black lines outline the hillslope
catchments. Along the blue lines, the in situ data (mound locations, soil
samples, vegetation mapping) were collected. (a) Position of the study sites
along the climate gradient. PdA – Pan de Azúcar, SG – Santa Gracia,
LC – La Campana, NA – Nahuelbuta. Positions of plots in (b) PdA, (c) SG,
(d) LC, and (e) NA. The background image is an RGB composite calculated from
WorldView-2 satellite imagery. Images were obtained with a single license from
GAF AG. Scale bar is the same for (b), (c), (d), and (e).
Methodology
We combined semi-empirical soil erosion modeling with in situ measurements,
remote sensing data, and machine learning methods (Fig. 2). Along eight hillslope
catchments within four climate zones, we mapped locations of burrows, estimated
the vegetation cover, and extracted soil samples. We analyzed the soil
samples in the laboratory. Then we used remote sensing datasets and machine
learning to upscale burrow distribution, vegetation cover, and soil
properties into the hillslope catchments. The hillslope catchment-wide
predictions, the topographical information retrieved from lidar data
(Kügler et al., 2022), and the climate information retrieved from climate
stations were the input parameters for our soil erosion model. We ran the
model with and without bioturbation. We included the bioturbation into the
model by adjusting the input parameters at the predicted burrow locations. We also included continuous burrowing activity and soil mixing (Grigusova et
al., 2021), the seasonality (Kraus et al., 2022), and the animal
phenological cycle as found in Jimenez et al. (1992). The models were
validated using self-constructed sediment traps. We studied the modeled
surface runoff and sediment redistribution. Lastly, we analyzed whether and how
the impact of bioturbation on sediment redistribution depends on
environmental parameters (topography, landscape connectivity, and
vegetation).
Flowchart of our study. Green indicates in situ input data;
blue indicates remote sensing input data. Red indicates model
parametrization. Yellow indicates model output and analysis. Gray indicates
model validation.
In situ data
The study setup consisted of eight hillslope catchments: one north-facing
and one south-facing hillslope catchment per study site. We defined a line
with a width of 1 m from the top to the base of each hillslope
catchment (see blue line, Fig. 1). We subdivided the track into tiles of 1 m2. We saved the GPS information of each tile.
Within each tile of the line, we mapped burrow presence, land cover, and the
extracted soil samples. A burrow consisted of an entrance and a mound (Fig. 3a). Each 1 m2 tile with a burrow was described as a presence data
point and tiles without a burrow as absence data points. We noted the size of
the burrow, vegetation cover, and land cover types (bare soil, herbs, shrubs,
trees) within the tile. We extracted 162 soil samples from soil without a
mound at a depth of 10 cm. Additionally, we took a photo of the surface
every second tile along the track.
To validate the model output, we set up sediment traps (Fig. 3b), with six
traps per site, two of which were located at the hillslope catchment base
and four were located on two random positions within the hillslope
catchment. The sediment traps consisted of geotextile and wooden poles and
had a length of 2–5 m. A total of 1.5 m of geotextile was laid horizontally down
at the surface, and 1 m of geotextile was vertically attached to wooden poles
to enable the collection of sediment (Fig. 3b).
The sediment accumulated within the traps was collected after 1 year, and its
mass (cm3) and dry weight (kg) were estimated.
Climate information was retrieved from climate stations located adjacent to
the hillslope catchments, which provide climate data in 5 min intervals
(Übernickel et al., 2021). To force the model on an hourly basis, hourly
air temperature, precipitation total and intensity, wind speed, wind
direction, and humidity were calculated for the study period from 1 April 2016 to 1 December 2021. Evapotranspiration was estimated with
the Penman–Monteith equation (Penman, 1948).
In situ constructions. (a) Example of a burrow consisting of
burrow entrance and mound. (b) Fence construction used for the collection of
eroded sediment to validate the model. Both photos by Paulina Grigusova.
Estimation of soil properties
We estimated several soil properties from the soil samples and photos
collected in situ (Grigusova et al., 2022). We estimated the rock coverage
on the surface and debris from the photos taken every second tile. For this,
the photos were firstly classified into five classes. The classification was
unsupervised using k means (Fig. A1). Then we calculated the ratio of pixels
classified as skeleton and/or debris to the overall number of all pixels
to determine the proportion of both parameters in percent.
In the lab, we estimated soil water content, bulk density, soil particle
density, soil texture (sand; silt; clay; coarse,
middle, and fine sand; coarse,
middle, and fine silt), soil skeleton, organic matter, and organic carbon.
Gravimetric soil water content (%) (GSWC) described the mass of water
within the soil sample and was estimated as in Eq. (1):
GSWC=Sm-SdSd×100,
where Sm (g) is the mass of moist soil measured directly after the
extraction and Sd (g) is the mass of soil dried at 105 ∘C for at
least 24 h. Bulk density (g cm-3) (BD) was calculated as follows:
BD=SdSv,
where Sv (cm-3) is the volume of the sample. Soil particle density (g cm-3) (SPD) was calculated as in Eq. (3):
SPD=dmSv,
where dm (g) is the dry mass of soil particles excluding pores.
Particle size distribution (%) of clay (< 0.002 mm); coarse,
middle, and fine silt (0.002 to 0.02 mm); and coarse, middle, and fine sand
(0.02 to 2 mm) was estimated according to Durner et al. (2017).
Soil skeleton was estimated as the ratio of particles with a diameter above
2 mm. Ratio of organic matter (OM) was estimated as in Eq. (4):
OM=1-ScSd,
where Sc is the weight (g) of the sample dried at 500 ∘C for 16 h.
We used pedotransfer functions to determine porosity, saturated soil
moisture, hydraulic conductivity, water content at field capacity, and
permanent wilting point. Pore ratio (θs) was estimated from bulk and
particle density as in Eq. (5):
θs=BDSPD.
Saturated water content (g g-1) (Ws) was estimated as in Eq. (6):
Ws=θspwBD,
where pw (g cm-3) is the density of water, which is set to be 1 g cm-3 (Pollacco, 2008).
Hydraulic conductivity Ks (m s-1) was estimated as in Eq. (7):
Ks=1.15741×0.0000001×exp(x),
where x for sandy soil is
x=9.5-1.471×BD×BD-0.688×OM+0.0369×OM×OM-0.332×CS,
and x for loamy and clayey soils is
x=-43.1+64.8×BD-22.21×BD×BD+7.02×OM-0.1562×OM×OM+0.985×lnOM-0.01332×C×OM-4.71×BD×CS,
where C is percentage of clay and CS is percentage of clay and silt
(Wösten, 1997). To estimate water content at field capacity (%) (FC)
and permanent wilting point (PWP), we applied functions by Tomasella et
al. (2000) as these were developed for South American soils:
10FC=4.046+0.426×Si+0.404×C,11PWP=0.91+0.15×Si+0.396×C,
where Si is the percentage of silt.
Processing of remote sensing data
The digital elevation models (DEMs) were calculated from the lidar data
(Kügler et al., 2022; Horn, 1981) at a resolution of 0.5 m. Slope was
calculated according to Horn (1981). Manning's surface roughness coefficient
was estimated following Li and Zhang (2001). The topographic position index
(TPI) and the topographic ruggedness index (TRI) were calculated according to
Wilson et al. (2007). To calculate the TPI, the average elevation of pixels
within a range specified by the user needs to be subtracted from the
elevation of the central pixel. Positive values represent hills, while
negative values represent valleys. The TRI adds together the elevation
differences between a grid cell and its eight neighbors. It measures the
relative level of topography irregularity: the higher the value, the more
irregular the topography. Plan and profile curvature were determined after
Zevenbergen and Thorne (1987). Connectivity indices, sinks, wetness index,
flow direction, flow path, catchment slope, and catchment were calculated in
SAGA GIS.
Single license stereo WorldView-2 images with a resolution of 0.5 m were
retrieved from GAF AG Munich GmbH. The topographic correction of WorldView-2
images was done using the lidar data, solar elevation angle, solar zenith
angle, and azimuth angle according to Goslee (2012). The digital surface
models (DSMs) were calculated from the stereo images. Additionally, we
extracted single bands and calculated the normalized difference vegetation
index (NDVI).
The erosion modelDaily Morgan–Morgan–Finney model
The DMMF model is a combined soil erosion model used to estimate surface
runoff and sediment flux on a field scale on a daily basis. Spatially, the
DMMF model represents an area as several interconnected elements (e.g.,
pixels) of uniform topography, soil characteristics, land cover type, and
vegetation structure. Through coupling, the model operates with flow
direction algorithms: each element receives water and sediments from upslope
elements and delivers the generated surface runoff and eroded soils to
downslope elements. On a temporal scale, the model estimates surface runoff
and sediment flux of each element on a daily basis. The model input
parameters include climate, topography, soil properties, and land cover
information (Choi et al., 2017). Data pre-processing, modeling, and analysis
(see Fig. 2) were done in the R statistical environment. The raster data were
cropped to the size of the hillslope catchments (Fig. 1). Input parameters
are listed in Table 1 and plotted in Fig. A2.
During the model simulation, water and sediment are transferred from pixels
located at higher elevations to pixels situated at lower elevations. This
occurs in two stages: the first stage is the hydrological phase where the
model calculates surface runoff, which happens when the amount of surface
water input exceeds the water-holding capacity. The amount of surface runoff
is computed by taking the infiltration capacity of the surface, the volume
of surface water input, and the fraction of the impervious area of a pixel
into account. Infiltration capacity represents the maximum amount of surface
water that can penetrate the subsurface layer. It is determined by the
percentage of the impervious area and the available pore space.
The second stage is the sediment phase, where the model estimates the
sediment budget for each particle size class, based on the surface
conditions. The model calculates the detachment and deposition of sediments
in a step-by-step process. The sources of sediments are detached particles
from the pixel itself due to rainfall and surface runoff and delivered soil
particles from higher-elevation pixels. The detachment of soil particles by
rainfall occurs when raindrops hit the ground with enough energy to detach
soil particles from the surface. Rainfall has different impacts on areas
with and without canopy cover, as canopy cover changes the kinetic energy of
raindrops.
The quantity of soil particles detached by raindrops is calculated based on
the soil particle detachability, the percentage of each particle size class,
the bare soil surface area, and the kinetic energy of effective rainfall.
The quantity of detached soil particles by surface runoff is calculated based
on the soil particle detachability, the amount of runoff, the slope angle of
the pixel, and the proportion of the bare surface area. The third source of
sediment is from higher-elevation pixels and is averaged by the surface area
of the pixel.
Once sediments are delivered to the surface runoff, a portion of the
suspended sediments settle to the bottom due to gravitational force. To
calculate this settling, the model requires the flow velocity of the runoff
and the settling velocity of each particle size class, which are influenced
by the flow depth, slope angle of the pixel, and Manning's roughness
coefficient (Choi et al., 2019).
Estimation of spatial parameters
For spatial parameterization of the DMMF model, we predicted land cover,
soil properties, and burrow distribution onto the hillslope catchments using
machine learning techniques. We used the approach of Meyer et al. (2018). The
most important predictors were selected by forward feature selection. The
quality of the random forest (RF) models was assessed by leave-location-out cross-validation. We trained the model stepwise, using in situ data collected from
seven of the hillslope catchments and validated the model using in situ data
from the remaining hillslope catchment (Meyer et al., 2018). The prediction
was done at 0.5 m spatial resolution. We used the WorldView-2 layers
obtained with a single license with GAF, NDVI, DEM, DSM, slope, and roughness
as predictors. The PAN-sharpening of the WV-2 layers was done by GAF. The
accuracy of the classifications was estimated by dividing the number of
correctly classified pixels to the number of all pixels.
For the area-wide prediction of burrow locations across the hillslope
catchments, we used the burrow presence and absence data (Sect. 3.1) as
the response data within the RF models. The accuracy was 0.82 for PdA, 0.77
for SG, 0.75 for LC, and 0.85 for NA. The prediction of soil properties was
done using soil properties estimated along the track line (see Sect. 3.1)
as response data within the RF models. All of the models reached a high
accuracy (see Table A1).
To obtain land cover classification, we used, as the response within the RF
models, the land cover measured in situ. The classes were soil without rocks,
rocks, biocrusts, grass/herbs, shrubs, and trees. Predictor values for each
class were extracted from at least 100 polygons per site and class. The
accuracy of the RF models was 0.71 for PdA, 0.81 for SG, 0.83 for LC, and
0.75 for NA.
The vegetation height measured in plots was averaged for each class per
site. All pixels classified as the respective class were assigned the same
vegetation height information. Vegetation density was estimated per
hillslope catchment as the number of vegetation individuals per square meter.
Vegetation diversity was calculated with the Shannon index (Shannon, 1948). The
interception area was the area not covered by vegetation (herbs, shrubs, or
trees).
Inclusion of bioturbation
In the grid cells with predicted burrow locations, we adapted the values of
input parameters to include bioturbation. The adaptations varied with
climate zone and burrow size. The size, geometric structure, and excavation
rates of burrowing animals were previously estimated at a high spatial and
temporal resolution (Grigusova et al., 2022). Based on these results, we
firstly adjusted the microtopography. We modified the layer depth to
represent burrow entrance and elevation to represent animal mound. Mounds
were always located downslope of burrow entrances in the direction of flow.
Secondly, we adjusted the soil properties. The soil properties texture and
organic carbon were estimated from soil extracted from mounds in Kraus et al. (2022). In this study we additionally estimated bulk density, initial
water content, soil skeleton, porosity, saturated water content, available
water capacity, and water content at field capacity from the same dataset
(see Sect. 3.2). We calculated the median value of each property for the
samples extracted from mounds and for the samples extracted from soil
without mounds. Then, we estimated the change in percent between these two
values. This was then used to adjust the soil property for each pixel
including a mound.
Thirdly, modeled mound pixels had to be cleared from ground vegetation
cover. For this, we removed ground vegetation cover from pixels with burrow
locations and decreased ground vegetation cover, height, diameter, and number
of ground vegetation individuals from adjacent pixels as measured in situ.
Then, the number of rocks and amount of debris were set as estimated from soil samples
(Sect. 3.2)
Animal activity has been found to be highly variable throughout the year
(Grigusova et al., 2022; Kraus et al., 2022). The density of burrows does
not stay stable throughout the year but increases or decreases depending on
the season and climate zone. We therefore artificially removed or added
burrows into the hillslope catchments during the particular seasons. For this,
we adapted the density of soil, the topography, and vegetation cover
accordingly. We created a 3D model of the burrow structure and adjusted
subsurface soil properties and properties of soil excavated to the surface:
the removed vegetation within the pixel with a predicted burrow and
decreased adjacent vegetation cover.
Lastly, we also included the vertical movement of sediment particles from
deeper soil layers to the surface depending on climate. Animals were
found to reconstruct their burrows after each rainfall event (Grigusova et
al., 2022). Corresponding with these findings, we increased the entrance
depth and mound height by 30 % after each rainfall event, which represents
the averaged value found in the previous study (Grigusova et al., 2022).
For the validation, we ran the model for the time periods between the
installation of sediment fences and the collections of sediment. We compared
the mass and weight of modeled and collected sediment and estimated R2
and RMSE. To test the importance of the inclusion of individual bioturbation
parameters into the model, we ran the model under four conditions: (i) no
burrows, (ii) solely entrances, (iii) solely mounds, and (iv) entire burrows
(entrances and mounds).
Model input layers and respective changes to layer values at the
predicted burrow locations. Ground vegetation was removed from the
respective pixels, while tree canopy was not changed. The values were
estimated as described in Sect. 3.5.2. Using the adjusted values, we calculated
evapotranspiration using the Penman–Monteith equation, surface roughness
from the elevation layer, hydraulic conductivity, water content at field
capacity, and saturated water content using pedotransfer functions.
Pixel value at burrow locations DerivationParameterUnitsPdASGLCNADEMElevationm a.s.l.+0.24+0.23+0.36+0.19Surface roughness–––––Depthm-0.23-0.41-0.22-0.04Soil samplesWater content%+120-6-68-62Bulk densityg cm-3–-6-17–Sand%-29-12+57-43Silt%+54+22+23nsClay%+145+44+19-73Organic carbon%+168+72+105+25PedotransferHydraulic conductivitym s-1––––functionsWater content at field capacity%––––Saturated water content%––––Land cover classificationGround vegetation cover%0000Soil and debris%100100100100Skeleton%0000Average plant heightm0000Average plant diameterm0000Number of plantsn m-20000
The ns means not significant.
DMMF model sensitivity test
We conducted a sensitivity test to identify those input parameters which
significantly influence the model output. For this, we first estimated the
mean value of each input parameter. Then, we created an artificial hillslope
catchment of 100 m × 100 m. To start the test, each pixel received the mean
value of each parameter. We ran the model for one rainfall event. Then, stepwise, we changed the single input parameter values from their minimum to
their maximum values while not adjusting any other parameters. To
quantify the significance of the input variations, we conducted a t test
(Table A2). For this, we compared the amount of redistributed sediment of
each model run to the first model run.
Impact of burrows on surface processes
We estimated burrow density as the ratio of pixels with predicted burrows to
all pixels. Additionally, we calculated the ratio of pixels which are part
of a burrow aggregation to all pixels which include a burrow. Burrow
aggregation describes at least four neighboring pixels with predicted burrows.
We calculated the amount of excavated sediment as a sum of burrow density
and the burrow excavation rate as estimated in Grigusova et al. (2022).
To estimate the impact of burrows on sediment redistribution and surface
runoff, we ran the DMMF model for the time period from 1 April 2016
to 31 December 2021 for all hillslope catchments. We ran the model (i) with no burrows and (ii) with entire burrows. We estimated (i) sediment
redistribution (accumulation minus erosion) and (ii) surface runoff. We analyzed
the redistribution and runoff on the plot (1 m2) and hillslope
catchment (1 ha) scale.
Lastly, to analyze under which biotic and abiotic environmental parameters
(topography, vegetation cover) the bioturbation enhances sediment erosion or
accumulation, we set up a generalized additive model (GAM) (Wood, 2006). For
this, we first subtracted the output of the model with no burrows from the
output of the model with entire burrows. Within each pixel, two processes
are happening simultaneously: a certain amount of sediment erodes, and a
certain amount of sediment accumulates. To estimate the sediment
redistribution for each pixel of each model run, we estimate which of these
processes dominated. Positive pixel values thus mean that bioturbation enhanced
sediment accumulation; negative pixel values mean that bioturbation enhanced
sediment erosion. We tested the following environmental parameters: mound
density, vegetation cover, elevation, slope, aspect, TRI, TPI, curvature and
connectivity, and wetness index. The model performance was evaluated by the
percentage of explained data variance. We analyzed the impact of
environmental parameters within 1 m and within 10 m from
the burrows.
ResultsModel sensitivity test and accuracy
Parameters which significantly influenced the model output were
precipitation, slope, vegetation cover, surface roughness, silt content, and
water content (Table A2). There was a correlation between some of the spatial
model parameters (Fig. A10), especially between the initial and saturated
water content, between water content and vegetation cover, and between clay
content and field capacity. However, a high correlation between spatial
parameters does not mean that these parameters impact the sediment
redistribution in a similar way.
We quantified the model performance by comparing the modeled and measured
sediment redistribution. The performance varied depending on the burrow
inclusion (Figs. 4 and 5). The performance of the model without any
bioturbation was lower (R2= 0.73, RMSE = 1.50, MSE = 2.27), as
when burrow entrances (R2= 0.81, RMSE = 1.34, MSE = 1.16) or
mounds (R2= 0.83, RMSE = 1.10, MSE = 1.22) were included. The
model had the highest performance when entire burrows were included (R2= 0.85, RMSE = 1.01, MSE = 1.01). However, as the scatterplots
showed, the model performance seemed to be determined strongly by one
measurement (Fig. 5). For this reason, we calculated the metrics without
this measurement (Fig. A2). The model without any burrows (R2= 0.17,
RMSE = 1.18, MSE = 1.39) in this case performed much lower than models
with burrows. The model performance increased when burrow entrances (R2= 0.48, RMSE = 0.61, MSE = 0.78) or mounds (R2= 0.51, RMSE = 0.75, MSE = 0.57) were included. The model with whole burrows reached
the highest performance (R2= 0.71, RMSE = 0.63, MSE = 0.39).
When we compare the modeled redistribution to the sediment redistribution
estimated using time-of-flight cameras in Grigusova et al., (2022), the
differences appear to be minor (R2= 0.62, RMSE = 0.12, MSE = 0.35).
R2 and RMSE of the Morgan–Morgan–Finney soil erosion model.
For dataset A, we compared the amount of sediment collected in all sediment
fences with the modeled eroded sediment (see Fig. A3). For dataset B, we
removed one measurement, as the R2 seemed to be defined by this
measurement (see Fig. A4). For scenario A, we did not include any burrows
into the model. For scenario B, we included burrow entrances, and for
scenario C, we included mounds. For scenario D, we included whole burrows
into the model. The adjustments made to include entrances, mounds, and
burrows into the model are described in Sect. 3.5.2.
Measured and modeled redistributed sediment without an outlier.
(a) Model without bioturbation. (b) Model with entrances. (c) Model with
mounds. (d) Model with burrows.
Model output: surface runoff and sediment redistribution
Hillslope catchment-wide sediment redistribution (1 ha resolution) was
the highest in humid NA, followed by Mediterranean LC, semi-arid SG, and arid
PdA (Figs. 6a, b, 8). In NA, LC, and SG, the erosion processes dominated,
while in PdA, more sediment accumulated than eroded. The impact of burrows
on sediment redistribution was significant in arid PdA, semi-arid SG, and
Mediterranean LC. Burrows increased sediment redistribution by 137.8 % ± 16.4 % in arid PdA (3.53 kg ha-1 yr-1 vs. 48.79 kg ha-1 yr-1), by 6.5 % ± 0.7 % in semi-arid SG
(129.16 kg ha-1 yr-1 vs. 122.05 kg ha-1 yr-1),
and by 15.6 % ± 0.3 % in Mediterranean LC (4602.69 kg ha-1 yr-1 vs. 3980.96 kg ha-1 yr-1). Overall, bioturbation
increased sediment accumulation in the arid zone (as the magnitude of the
sediment excavation by the animals exceeded sediment erosion which occurs
during rainfall events), but it increased sediment erosion in semi-arid and
Mediterranean climate (where animal burrowing activity and rainfall are
present). The largest impact was found under Mediterranean conditions. We
found no significant effect on redistribution in the humid zone (Fig. 7).
However, impact of bioturbation varied throughout the hillslope catchment
(Figs. 7, 8, and 9).
Surface runoff was the highest in humid NA, followed by Mediterranean LC,
arid PdA, and semi-arid SG (Fig. 6c). The impact of burrows on surface
runoff was significant in all climate zones. Burrows increased surface
runoff in PdA by 34 %, in SG by 40%, and in LC by 4.1 %, but it
decreased surface runoff by 5.9 % in NA. Hillslope catchment-wide maps
are shown in Figs. A6–A8.
Summary of model outputs across the climate gradient. PdA is arid
Pan de Azúcar. SG is semi-arid Santa Gracia. LC is Mediterranean La
Campana. NA is humid Nahuelbuta. Graphs (a) and (b) show the modeled
sediment redistribution. Positive values indicate sediment accumulation;
negative values indicate sediment erosion. In (a) sediment redistribution is
shown on a pixel scale (in kg m-2 yr-1), while in (b) sediment
redistribution is shown on the hillslope catchment scale (in kg ha-1 yr-1). The impact of bioturbation on sediment redistribution was
estimated by a t test and was significant in three sites: PdA***, SG**, and
LC***. Bioturbation increased sediment redistribution by 137.8 % in PdA,
by 6.5 % in SG, and by 15.6 % in LC. For hillslope catchment-wide maps
see Figs. A6–A8. Graph (c) represents the modeled surface runoff on the
hillslope catchment scale (in m3 ha-1 yr-1). The impact of
bioturbation on surface runoff was estimated by a t test and was significant
at all sites. Bioturbation increased surface runoff in PdA by 34 %, in SG
by 40 %, and in LC by 4.1 %, but it decreased surface runoff by 5.9 %
in NA. For hillslope catchment-wide maps, see Fig. A6.
Comparison of the model outputs with and without bioturbation of
each pixel (0.5 m) in all study sites. The x axis shows the output of the
model with bioturbation, and the y axis shows the model output without bioturbation.
PdA is arid Pan de Azúcar. SG is semi-arid Santa Gracia. LC is
Mediterranean La Campana. NA is humid Nahuelbuta. Points represent single
pixel values; lines show linear regressions for the sites. The lower the R, the
higher the impact of burrows is on sediment redistribution at the resolution of
0.5 m. The dashed black line symbolizes a perfect correlation – along this
line the bioturbation would have no effect on sediment redistribution.
Bioturbation leads to more accumulation if the regression line representing
results from a particular climate zone is steeper than the perfect
correlation line. Bioturbation leads to more erosion if the regression line
representing results from a particular climate zone is flatter than the
perfect correlation line. Bioturbation increases sediment accumulation in
arid PdA (through the high burrowing rate, more sediment is accumulated on
the surface than eroded during rainfall events). Bioturbation increases
sediment erosion in semi-arid SG and Mediterranean LC. In absolute terms, the
highest impact on sediment redistribution is in the Mediterranean climate
zone. The lowest impact is in the humid zone.
Hillslope catchment-wide predicted sediment redistribution.
Colors indicate sediment redistribution. Gray indicates the hill
shading calculated from lidar data. (a) Pan de Azúcar, (b) Santa Gracia,
(c) La Campana, and (d) Nahuelbuta.
Role of continuous burrowing activity on sediment redistribution
We included transport of the sediment to the surface by animal excavation
into the model. The density of burrows was the highest in the arid PdA, then
Mediterranean LC, and then semi-arid SG, and it was the lowest in humid NA. Burrows were
mostly distributed within groups of several burrows in Mediterranean LC and
semi-arid SG, while they were more evenly distributed in the arid PdA and
humid NA. The burrows were of the largest in Mediterranean LC, followed by
arid PdA, semi-arid SG, and humid NA. Similarly, the highest volume of
excavated sediment at the beginning of the modeling period was in
Mediterranean LC and arid PdA. The volume of excavated sediment during the
burrow reconstruction after rainfall events was the highest in humid NA,
followed by Mediterranean LC, semi-arid SG, and arid PdA. The percentage of
sediment excavated by the animal to sediment redistributed during rainfall
events was 128 % in PdA, 24 % in SG, 33.5 % in LC, and 5.6 % in
NA.
Impact of animal bioturbation activity on overall sediment
redistribution on various scales. The bioturbation activity was estimated
using time-of-flight-based cameras in Grigusova et al. (2022). This study
showed that animals reconstruct their burrows after each rainfall event.
During this process, 10 % of the overall sediment burrow volume is
relocated from within the burrow to the surface. We integrated this process
into our model and calculated the percentage of newly excavated sediment by
the animals to the amount of sediment which was redistributed during
rainfall for the period of 1 year.
ParameterUnitsPdASGLCNABurrow densityha-191.3571.5084.3613.30Burrow aggregations%2462735Burrow sizem30.0150.0120.0470.008Sediment at the surface at the start of modelingm3 ha-11.350.884.110.10Sediment excavated after each rainfallm3 ha-10.070.040.220.01Number of rainfall eventsyr-13716137Sediment excavated by the animal after the rainm3 ha-1 yr-10.210.283.520.69Sediment redistributed due to rainfallm3 ha-1 yr-10.441.1710.5112.21Excavated sediment to redistributed sediment%472433.55.6Role of adjacent environment
We subtracted the output of the model with included burrows from the output
of the model without burrows (Fig. A8). Although the burrows on average
enhanced sediment erosion on the hillslope catchment scale, the
high-resolution maps unveiled that burrows enhance sediment erosion within
some pixels, while they rather increased sediment accumulation within others.
The amount of data variance explained by the GAM models (see Sect. 3.6.)
differed between models (Table A3). Models estimating the impact of
environmental parameters on sediment redistribution within 1 m
from the burrows explained 3.84 % of the variance in PdA, 37.1 % in SG,
46 % in LC, and 42. % in NA. Models estimating the impact of
environmental parameters on sediment redistribution within 10 m
from the burrows explained 1.99 % of the variance in PdA, 12.8 % in SG,
52 % in LC, and 72.9 % in NA. The parameters selected for SG were
slope, roughness, curvature, TRI, and NDVI. Parameters selected for LC were
elevation, slope, NDVI, sinks, and roughness. Parameters selected for NA were
elevation, slope, aspect, TRI, sinks, and roughness (Fig. 10).
Bioturbation strongly increased sediment redistribution (erosion and
accumulation) at high values of elevation, slope, surface roughness, TRI,
sinks, and topographic wetness index; at the middle values of elevation and
aspect; and at low values of profile curvature and NDVI. From these
parameters, bioturbation increased sediment erosion at high and middle
values of elevation; at high values of slope, sinks, and TRI; and at low
values of profile curvature. Bioturbation increased sediment accumulation at
high values of surface roughness and topographic wetness index and at low
values of NDVI (Figs. A3–A8).
Bioturbation somewhat enhanced sediment erosion at medium values of surface
roughness, NDVI, and sinks and at low values of topographic wetness index.
Bioturbation somewhat increased sediment accumulation at low values of slope
and TRI, at low and medium values of elevation, and at high values of profile
curvature.
Hillslope catchment-wide impact of bioturbation on sediment
redistribution. Color indicates the impact. Positive values indicate that
bioturbation enhanced sediment accumulation; negative values indicate that
bioturbation enhanced sediment erosion. Gray indicates the hill
shading calculated from lidar data. (a) Pan de Azúcar, (b) Santa Gracia,
(c) La Campana, and (d) Nahuelbuta.
This figure is a conceptual summary of the detailed results from
Figs. A3–A8. Bioturbation increases erosion or accumulation depending
on the values of environmental parameters. The dependencies are the same for
all climate zones. The figure is the conceptual summary for all climate
zones; therefore, there are no values stated on the x and y axes. The
x axis shows whether bioturbation increases erosion or accumulation. The y axes
are environmental parameters. Line thicknesses indicate the magnitude of
impact. Please note that bioturbation has no impact on sediment
redistribution in regions with low sink connectivity and topographic
ruggedness. The relationship between the values of environmental parameters
and the impact of bioturbation is not linear: bioturbation can have the same
impact on sediment redistribution at high or low values of an environmental
parameter but a contrasting impact at middle values of this parameter (as
in this case for elevation, slope, or surface roughness).
DiscussionThe inclusion of bioturbation increases model performance
Overall, our DMMF model including bioturbation performed much better than
the model without bioturbation. The DMMF model without bioturbation
performed worse (RMSE of 1.18 kg ha-1 yr-1 and R2 of
0.17) than the model with bioturbation (RMSE was 0.63 kg ha-1 yr-1 and R2 was 0.71).
We hence argue that the higher accuracy of our model can be explained with
the inclusion of bioturbation. This is confirmed by the fact that our model
run without bioturbation performed similarly to previously run models
without bioturbation: in earlier studies, the accuracy of the MMF model
reached an RMSE between 4.9 and 8.2 kg ha-1 yr-1, with an
estimated R2 of between 0.21 and 0.57 (Jong et al., 1999; Vigiak et
al., 2005; López-Vicente et al., 2008; Vieira et al., 2014; Choi et al.,
2017). However, we acknowledge that previous studies were all conducted in
more temperate climate zones. To be able to compare our results with
previous studies, we calculated the model performance considering solely the
Mediterranean and humid climate zone, which are more similar in climate to
the more temperate locations of previous studies. The performance of the
model was still high (R2= 0.72, RMSE = 0.45 kg ha-1 yr-1), confirming the conclusion that bioturbation increased model
performance.
We compared the modeled impact of bioturbation on sediment redistribution
with the impact of bioturbation estimated in previous studies. In the humid
zone, our model predicted an erosion up to 3.5 kg m-2 yr-1. This
estimation is in line with erosion rates established by in situ measurements
in other studies conducted in a more humid climate zone (between 1.5 and 3.7 kg m-2 yr-1) (Black and Montgomery,
1991; Yoo and Mudd, 2008; Yoo et al., 2005; Rutin, 1996). This also confirms
the reliability of our approach. Previous authors estimated the impacts
using rainfall simulators, erosion pins, or splash boards. The measurements
were conducted for a time period between 3 months and 3 years, and the sites
were revisited for each estimation. We do not compare our results with
studies which previously applied models to estimate impacts of bioturbation,
as, to our knowledge, none of the previous studies integrated vertebrate
burrow structures into a soil erosion model and ran the model on a daily
basis.
The relevance of bioturbation for sediment redistribution depends on the
environmental context
On the hillslope catchment scale (1 ha), our study finds that bioturbation
increases erosion in semi-arid and Mediterranean zone, increases accumulation in the
arid zone, and has no impact within the humid zone (Fig. 6b). In contrast,
bioturbation increases both erosion and accumulation on the plot scale (1 m2) (Fig. 6a). On this scale, in the arid and semi-arid zone,
sediment erosion and accumulation were predicted to be about equal (erosion
and accumulation both up to 0.1 kg m-2 yr-1 in the arid zone
and erosion and accumulation both up to 0.2 kg m-2 yr-1 in the
semi-arid zone; see Fig. 6a). Bioturbation marginally increased erosion
and decreased accumulation in the semi-arid zone but reduced
accumulation 2-fold in the arid zone. In contrast, in the Mediterranean and humid
zone, erosion was predicted to be almost double when compared to
accumulation (predicted erosion up to 2.5 kg m-2 yr-1 and
accumulation up to 1.4 kg m-2 yr-1). Inclusion of bioturbation
increased erosion up to 3 kg m-2 yr-1, and accumulation up to
1.6 kg m-2 yr-1 in the Mediterranean zone, while it had no
significant effect in the humid zone. We argue that sediment redistribution
due to bioturbation is heavily influenced by mesotopographic structures
which determine the flow path of surface runoff and influence the
infiltration processes. Due to this, the erosion and accumulation on the
plots scale are more heavily impacted by bioturbation with increasing surface
runoff.
Our study found an increase of erosion in the semi-arid and Mediterranean
climate zone to be between 6.5 % and 15.6 % due to bioturbation.
Previous studies found that even a small increase of erosion has
significant impacts on the whole hillslope catchment. A 10 % increase in
erosion rates over a 10-year period can lead to significant changes in the
landscape, including, for example, a 20 %–30 % reduction in soil thickness and an
increase in sediment transport in nearby rivers (Kuhn, 2016).
According to our analysis, bioturbation increases erosion or accumulation of
sediment mostly based on an interplay between topographic structures
elevation, slope, and TRI (Fig. 10). Over all research sites, this study
found that bioturbation leads to an increase in surface erosion in areas
where erosional processes dominate (upper and/or steeper slopes) and tends
to increase sediment accumulation in areas where sediment is naturally
deposited (e.g., lower slopes or shallow depressions; Fig. 10). This
finding is based on the fact that erosion in general is positively affected
by slope and negatively by surface roughness and vegetation
(Rodríguez-Caballero et al., 2012; Wang et al., 2013; Kirols et al.,
2015). Additionally, the redistribution of sediment is largely affected by
topographic meso-/macroforms, such as rills or cliffs. These can be
quantified by topographic ruggedness index (TRI), which describes the amount
of elevation drop between adjusting cells of DEM (Wilson et al., 2007). At
high values of this index, we would therefore expect high erosion rates, due
to concentrated runoff within the connected rills or undisturbed flow of
runoff from the cliffs downslope.
Our data show that one burrow provides up to 0.43 m3 of additional
loose sediment at the surface (Table 2), while the surface roughness
increases up to 200 % (Grigusova et al., 2022). When including burrows
into the model, with slope values from 0 to 5∘, the presence of
burrows had no impact on sediment redistribution. From 5∘ onwards it
increased sediment erosion proportionally to the slope of the hillside (an
increased erosion from 0.4 g ha-1 yr-1 in the semi-arid zone
to 150 kg ha-1 yr-1 in the Mediterranean zone; Figs. A3–A6). Similarly, at locations with elevation drops ranging from 0 m
to 0.2 m (lower TRI values), the presence of burrows had no impact.
However, at locations with elevation drops of 0.2 to 0.5 m (higher TRI
values), bioturbation increases sediment erosion by 1.5 kg ha-1 yr-1 (Figs. A3–A8). Lastly, bioturbation proportionally increased
accumulation when the surface roughness values were above 0.5 (an increased
accumulation from 0.2 g ha-1 yr-1 in semi-arid zone to 5000 kg ha-1 yr-1 in the Mediterranean zone; Figs. A3–A6).
We conclude that, in locations with slope values over 5∘ or at
locations with sudden drops in elevation (high TRI) and connected rills,
more sediment is eroding than accumulating. Here, additional surface
sediments generated by bioturbators provide more source material for
erosion, and thus bioturbation increases sediment erosion at these locations
(Figs. 10 and 11). In contrast, at locations with a slope below 5∘,
where processes are dominantly controlled by surface roughness, sediment
accumulation caused by bioturbation increases proportionally when the
surface roughness has a value above 0.5. This is likely because burrows
through their above-ground structures heavily increase surface roughness
(Grigusova et al., 2022), and hence the presence of bioturbating animals
leads to an increase in sediment accumulation.
Additionally, we hypothesize that it is not only the additional availability
of sediment on the surface and the topography of the vicinity which controls
the contribution of bioturbation to sediment surface flux, but also the
spatial distribution of animal burrows. We interpret that, in locations with
high burrow aggregation, surface flow might be redirected and centralized
around the aggregates and thus increase sediment erosion in the areas
adjacent burrow aggregates (Fig. 11). This mechanism could explain why
bioturbation promotes sediment erosion especially in the Mediterranean zone,
where burrows are more aggregated. The relative role of burrow aggregation
should be studied in detail and included in future studies.
Context dependency of sediment redistribution. (a) Pan de
Azúcar, (b) Santa Gracia, (c) La Campana, and (d) Nahuelbuta. Brown
arrows indicate the direction and magnitude of overall sediment
redistribution within each climate zone. Blue arrows indicate the direction
of flow (runoff vs. infiltration). Semicircles indicate the distribution and
size of the burrows. The dashed line indicates the median value of each
parameter for the first four parameters.
Conclusions
Our study found that the inclusion of vertebrate bioturbators' burrows into
a soil erosion model significantly increases its reliability. Vertebrate
bioturbators increase sediment accumulation in the arid climate zone, increase
sediment erosion in the semi-arid and Mediterranean zone, and have no impact
on sediment redistribution in the humid zone. Our study furthermore shows that
the impact of bioturbation heavily depends on adjacent environmental
parameters. The burrows increase sediment erosion at high and low values of
elevation; at high values of slope, sink connectivity, and topography
ruggedness; and at low values of vegetation cover. The burrows increase
accumulation at high values of surface roughness and soil wetness. This
means that, overall, on geological timescales, as burrowing animals increase
both erosion in steeper zones and accumulation in areas with gentler
slopes and higher roughness, hillslope relief should become more quickly equalized
and overall more flat. This tendency is most pronounced in the
Mediterranean zone with high burrow density and excavation rates, as well as
comparably high precipitation rates.
R2 and RMSE of random forest models trained for the
prediction of soil properties needed for model parametrization. RMSE is root
mean square error.
VariableR2RMSESoil water content0.800.05Bulk density0.600.22Porosity0.630.09Silt0.640.04Middle silt0.640.04Sand0.680.09Middle sand0.640.05Organic components0.770.05Organic carbon0.700.03
Model sensitivity analysis. For the analysis, the minimum, maximum,
and mean values of each parameter were calculated. The model was run for a
hillslope catchment of 1 km2 with homogenous mean parameters. Then, the
minimum and maximum values of each parameter were tested. Each parameter was
changed stepwise to its minimum or maximum value while the remaining
parameters stayed homogenous. The significance of the parameter was
estimated by a t test conducted between the erosion estimated by the model
with homogenous mean parameters and the erosion estimated by the model with
varying minimum and maximum parameter values. Only significant parameters
are shown.
Summary of GAM models. We analyzed the impact of parameters within
a 1 and 10 m from burrows. The asterisks indicate p values of
the selected parameters. ****p<0.001. ***p<0.01. **p<0.05. *p<0.1. One GAM model was run per parameter. Only
results for models with an explained variance above 5 % are shown.
ParametersWithin 1 m from burrows Within 10 m from burrows PdASGLCNAPdASGLCNAExplained Variance3.8 %37 %46 %42 %2.0 %13 %52 %73 %Burrow density**Elevation****************Slope*********Aspect*********Roughness*********TPITRI******Plan curvature**Profile curvature****NDVI*******Sinks**********Wetness***Flow directionFlow pathCatchment****Catchment slope*****
An example of the unsupervised k-means classification of the surface
photo from La Campana. Original photo was taken by Paulina Grigusova. The
collection of in situ data is explained in Sect. 3.1. and the estimation of
soil properties in Sect. 3.2. The image was classified into five classes
using unsupervised k-means classification; the land cover was then assigned
manually. In some cases, like in this case for rocks, multiple k-means
classes stand for the same land cover. These were then unified to the class
“rocks”.
Review of studies which integrated any kind of bioturbation into
models. Previous models integrated either benthic, invertebrate, or single
species of vertebrate bioturbators. Models applied described either the
vertical soil mixing or long-term landscape evolution models. None of the
previous studies included vertebrate burrows of bioturbators into an erosion
model which would be capable of capturing the daily redistribution processes.
ReferencesBioturbatorsIntegrated processesTargeted processModelFrancois et al. (1997), Francois et al. (2002), Kadko and Heath (1984), Croix et al. (2002), and several othersVarious benthic bioturbatorsEquations describing soilmixing within a floodplainVertical soil mixing within a floodplainMathematical equationsOrvain et al. (2006), Román-Sánchez et al. (2019), Orvain (2005), Orvain (2003), Sanford (2008), and several othersVarious invertebratesEquations describing verticalsoil mixingInfluence of vertical soil mixing on lateral redistributionMathematical equationsGabet (2000)Pocket gophersEquation describing diffusioncaused by gopher bioturbationRelief changes over 40 000years, lateral redistributionLandscape evolutionGabet et al. (2014)Pocket gophersEquations describing sediment accumulation causedby gophersRelocation of sedimentto create Mima moundsLandscape evolutionTemme and Vanwalleghem (2016)Not specifiedinvertebratesBioturbation causes soil mixing between model layers. Mixing is proportional to depth in the profile, soil thickness, and soil carbon content, and layer distanceSoil and landscape evolutionLandscape evolutionVanwalleghem et al. (2013)Landscape evolutionYoo and Mudd (2008)Bioturbation is considered as the cause of colluvial transport. Colluvial fluxes are calculated as a function of soil thickness and slope gradient on sloping groundsLandscape evolutionPelletier et al. (2013)Vertical soil mixing. Rate increases linearly with aboveground biomass.Creep including abiotic andbioturbation-driven transportLandscape evolutionVan der Meij et al. (2020)Vertical soil mixing. Rate depends on vegetation type.Soil and landscape evolutionLandscape evolutionOur modelVertebratesThe model includes burrow structure, adjusted soil properties and adjusted vegetation cover. Burrow distribution determined by machine learning.Daily lateral sedimentredistributionDaily erosion model
Measured and modeled redistributed sediment for different
scenarios. (a) Model without bioturbation. (b) Model with entrances. (c)
Model with mounds. (d) Model with burrows.
Environmental parameters influencing impact of bioturbation on
sediment redistribution in Santa Gracia within 1 m from the
burrows. Positive values indicate bioturbation enhances sediment
accumulation at the respective parameter values; negative values indicate
bioturbation enhances sediment erosion at the respective parameter values.
Environmental parameters influencing impact of bioturbation on
sediment redistribution in Santa Gracia within 10 m from the
burrows. Positive values indicate bioturbation enhances sediment
accumulation at the respective parameter values; negative values indicate
bioturbation enhances sediment erosion at the respective parameter values.
Environmental parameters influencing impact of bioturbation on
sediment redistribution in La Campana within 1 m from the burrows.
Positive values indicate bioturbation enhances sediment accumulation at the
respective parameter values; negative values indicate bioturbation enhances
sediment erosion at the respective parameter values.
Environmental parameters influencing impact of bioturbation on
sediment redistribution in La Campana within 10 m from the burrows.
Positive values indicate bioturbation enhances sediment accumulation at the
respective parameter values; negative values indicate bioturbation enhances
sediment erosion at the respective parameter values.
Environmental parameters influencing impact of bioturbation on
sediment redistribution in Nahuelbuta within 1 m from the burrows.
Positive values indicate bioturbation enhances sediment accumulation at the
respective parameter values; negative values indicate bioturbation enhances
sediment erosion at the respective parameter values.
Environmental parameters influencing impact of bioturbation on
sediment redistribution in Nahuelbuta within 10 m from the burrows.
Positive values indicate bioturbation enhances sediment accumulation at the
respective parameter values; negative values indicate bioturbation enhances
sediment erosion at the respective parameter values.
Burrow aggregation concentrates the runoff and increases erosion.
An example of a north-facing hillside in Mediterranean La Campana for the
time period of 1 year. (a) Sediment erosion as estimated by the model without
bioturbation. (b) Sediment erosion as estimated by the model with bioturbation.
(c) Sediment erosion as estimated by the model with bioturbation with predicted
burrow locations. (d) Surface runoff as estimated by the model without
bioturbation. (e) Surface runoff as estimated by the model with bioturbation.
(f) Surface runoff as estimated by the model including bioturbation and
predicted burrow locations. Black indicates that at least one burrow was
located within this pixel. Four neighboring pixels which contain a burrow
form a burrow aggregation.
Correlation matrix between the model input parameters.
Code and data availability
The estimated soil properties
(10.5678/wsrb-9f70, https://vhrz669.hrz.uni-marburg.de/lcrs/data_pre.do?citid=523, Grigusova, 2023),
modeled sediment redistribution (10.5678/32wa-d179, Grigusova, 2023b, https://lcrs.geographie.uni-marburg.de/lcrs/data_pre.do;jsessionid=22F870744C71E3DAB58C6201A5026656?citid=521),
and model code
(https://gitlab.uni-marburg.de/fb19/ag-bendix/model-sediment-redistribution-caused-by-bioturbating-animals, Grigusova, 2023c)
were published by LCRS data services.
Author contributions
PG set up the model, analyzed the data, and wrote the
manuscript draft; PG and AL performed the measurements; AL, JB, NF, RB, DK,
PP, LP, and CdR reviewed and edited the manuscript.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “Earth surface shaping by biota (Esurf/BG/SOIL/ESD/ESSD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
We thank CONAF for the kind support provided during our
field campaign. We thank our two reviewers for their valuable comments
that helped to significantly improve the manuscript.
Financial support
This study was funded by the German Research Foundation, DFG (grant
nos.
BE1780/52-1, LA3521/1-1, FA 925/12-1, and BR 1293-18-1) and is part of the DFG
Priority Programme
SPP 1803: EarthShape: Earth Surface Shaping by Biota, sub-project “Effects
of bioturbation on rates
of vertical and horizontal sediment and nutrient fluxes”.
Review statement
This paper was edited by Todd A. Ehlers and reviewed by Emmanuel Gabet and one anonymous referee.
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