Fig 1.
a) Acoustic sensors marked with black triangles are located at plots 7 (irrigated/irrigation stop) and 8 (control) of the irrigation experiment in Pfynwald, Valais, Switzerland; white triangles are control sensors in the air. b) Local setup of acoustic and microclimatic sensors: 1 acoustic sensor; 2 preamplifiers; 3 soil moisture and temperature; 4 surface temperature and light intensity. c) Case with computer and audio interfaces. Adapted from the map of the irrigation project Pfynwald (Orthomosaic © 2019 WSL, swissimage © 2014 swisstopo).
Fig 2.
Spectrogram of a 4-channel recording from the Pfynwald soil (sensors B1–4, irrigated plot).
Bright green waveforms on top: Display of the audio waveforms channels 1–4. Red/purple colored channels: Spectrograms channels 1–4, aligned from top to bottom. x axis: time, y axis: frequency. Channels 1–3 indicate sensor locations B1–B3 in Fig 1A). B4 is a control sensor in the air next to sensor B3. Channel 1 shows the movement sounds of a soil animal.
Fig 3.
a) Observed ACI values from two sensors (B1, K1) over the study period. Each day is represented by a horizontal line, with colors indicating the ACI at a certain time (measured every 10 min). White horizontal lines indicate transitions between seasons. Dark grey areas show missing or excluded data. Data on rain and irrigation were excluded, which is, for example, evident for the irrigation events at sensor B1 in the evenings in spring and summer. b) Predicted values from hierarchical models analyzing the effect of daytime, season, and their interaction on ACI. Colors indicate seasons. Points show the highest maximum a posteriori estimates, and error bars show the 95% highest density intervals. Data were aggregated at intervals of 6 h prior to analyses.
Fig 4.
Example of diurnal dynamics in microclimatic parameters and ACI during three days (26.06.2019–28.06.2019) for sensor B1.
Temperature and soil moisture are represented on the left axis, whereas ACI is represented on the right axis. Soil temperature and moisture were measured at 10 cm depth. These three days show a general pattern that may also be found in other periods (S12 Fig).
Fig 5.
Predicted values from hierarchical models analyzing the effect of microclimate on ACI.
Four microclimate variables were included in the models. Surface temperature change denotes the positive temperature change 30 min prior to the ACI measurement. For each variable, predictions are shown for a scenario at a low level (mean–1SD) and at a high level (mean + 1SD). For surface temperature change, the low-level scenario was set to 0 degrees temperature change to keep scenarios realistic. For soil moisture, a quadratic term was included in the model to account for a potential non-linear effect. Points show the highest maximum a posteriori estimates, and error bars show the 95% highest density intervals. Lines in black show the global mean, with its 95% highest density interval.
Fig 6.
Ordination graph (NMDS, first two axes) of soil communities based on Bray-Curtis distances (k = 3, stress = 0.174).
The table includes the results of a PERMANOVA testing for differences in taxa composition between seasons based on 1000 permutations.
Table 1.
Results of linear mixed-effects models on the effect of season (spring, summer, fall, winter) on the abundance and diversity of soil communities sampled from soil cores.
Fig 7.
Relationships between soil community attributes (abundance, taxa richness, Shannon, Simpson, community composition–NMDS 1/2), based on soil cores sampled at sites of different microclimatic conditions on six days in four seasons in 2018 and 2019, and ACI right before sampling, the mean of 1 h around sampling, and 24 h around sampling.
a, b: Conditional and marginal R2-values of linear mixed-effects models (fixed-effect soil community attribute), Significance is indicated by stars (* = p <0.05, ** = p <0.01) and the direction by signs (+, -); c, d: predicted relation between taxa richness / Community composition (NMDS1) and ACI before sampling incl. 95% confidence bands.