Acoustic analyses

Soundscape characteristics in relation to different levels of degradation and stages of restoration in the savannah ecosystem

Methodology

The spatial design will adhere to the Land Degradation Surveillance Framework (LDSF), as stated by (Vågen & Winowiecki, 2023). i) large-scale monitoring in the entire Northern Maasai Mara site (10,000 hectares) to assess soundscapes across a gradient of land degradation ii) smaller-scale monitoring within EnarauConservancyy (121 hectares) to monitor soundscapes at different stages of restoration.

At a large scale, Song Metre acoustic recorders (Wildlife Acoustics Song Metre Mini Bats – with the additional acoustic microphone: Wildlife Acoustics, Inc., Massachusetts, USA) were deployed at the centre of the 16-1 square kilometre in 2 dry and wet seasons.

At a smaller scale, the recorders were placed at the centre of a 500 by 500 metre grid.

#### Analysis

Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Normalised Difference Soundscape Index (NDSI), and Bioacoustic Index (BAI), soundscape saturation, were calculated to determine the complexity of species in each soundscape.

Kaleidoscope Pro Version 5.6.6 was used to apply Acoustic Index Analysis to audio recordings Kaleidoscope Pro.

R Version 4.4.1 was used to analyse the soundscape index for each cluster in each season.

Load the necessary libraries then check the data structure.

R package vegan calculates the diversity and dissimilarity measures

R package lme4 calculates the mixed-effects models

R package emmeans calculates post-hoc tests

library(tidyverse)
library(readr)

Loading data

data <- read_csv("data/acousticindex.csv")

head (data)

# A tibble: 6 × 13
  FOLDER `IN FILE` CHANNEL OFFSET DURATION DATE       TIME      HOUR  NDSI   ACI
  <chr>  <chr>       <dbl>  <dbl>    <dbl> <date>     <time>   <dbl> <dbl> <dbl>
1 "JUNE… SMU10314…       0      0     3.61 2024-06-15 19:07:12    19 0.976  149.
2 "APRI… SMU10336…       0      0     3.14 2024-04-15 23:48:55    23 0.994  153.
3 "JUNE… SMU10335…       0      0     4.74 2024-06-09 20:14:23    20 0.939  150.
4 "JUNE… SMU10338…       0      0     3.37 2024-06-11 21:09:05    21 0.645  173.
5 "APRI… SMU10317…       0      0     5.38 2024-04-14 01:43:03     1 0.946  152.
6 "JUNE… SMU10310…       0      0     8.65 2024-06-13 22:23:45    22 0.972  150.
# ℹ 3 more variables: ADI <dbl>, AEI <dbl>, BI <dbl>