require(knitr)
    require(png)
    require(dplyr)
    require(stringr)
    require(metafor)
    require(compute.es)
    require(kableExtra)
    require(pander)
    require(ggplot2)
    require(ggtext)
    require(RColorBrewer)
  
    source("src/acoustic_indices_functions.R")

This web page is a detailed description of the procedures followed to conduct the systematic review and meta-analysis published in Biological Reviews. Our aim was to evaluate the performance of the commonly used acoustic indices as proxies for biodiversity. The repository used to create this document can be found here: https://github.com/irene-alcocer/Acoustic-Indices

Figures and tables in this web page, follows the numbering used in the manuscript and supplementary material.

Systematic review

We extensively searched existing literature for studies assessing the use of acoustic indices as proxies for biodiversity. The systematic search proceeded as follows:

  1. We compiled studies that used acoustic diversity indices from three recent literature reviews on acoustic indices, biodiversity assessment and passive acoustic monitoring (i.e., Sueur et al. (2014); Buxton et al. (2018); Sugai et al. (2019)).
  2. We updated the literature database from 2017 up to July 2019 with Thompson’s ISI Web of Science (WoS), querying the database for the keywords (i.e. bioacoustic∗ AND ind∗, ecoacoustic∗, acoustic∗ AND biodiversity, soundscape AND ecology). This search was restricted to 9 WoS subject areas (i.e., zoology, environmental sciences ecology, behavioural sciences, biodiversity conservation, marine freshwater biology, acoustics, evolutionary biology, entomology, and remote sensing).
  3. Additionally, we used Google Scholar to compile all literature from 2017 to July 2019 that (i) cited any of these new papers published within this period or (ii) cited the papers originally describing the 11 selected indices (see Table 2 on the manuscript).

Both peer-reviewed and no peer-reviewed studies were included to avoid publication bias.

After removing all duplicates, we gathered a total of 1,079 studies.(See Figure 2 in manuscript).

Inclusion criteria

We considered studies eligible for meta-analysis if they met the following inclusion criteria:

  1. reported data to test the efficiency of acoustic diversity indices as indicators of biodiversity.
  2. employed at least one of the selected acoustic indices (ACI, ADI, AEI, AR, BIO, H, Hf, Ht, M, NDSI, NP).
  3. employed at least one of these five metrics to describe biodiversity, namely species abundance, species richness, species diversity (i.e., Shannon index, Simpson index, and Pielou’s evenness), abundance of sounds, and diversity of sounds; and
  4. provided statistical or graphical information of a univariate relationship between a single acoustic index and a single diversity metric.

Following such inclusion criteria, we screened all selected studies (n = 1,079) based on their abstract, titles and keywords and thereby retaining 142 studies which were identified as potentially eligible. To ascertain their relevance, we conducted a full-text assessment on all of these studies, finally retaining 35 studies that passed through all the criteria.

knitr::include_graphics("rmd/Fig.S1.png")

Figure 3 - Temporal evolution (2007–2019) of validation data from a total of 142 articles. Articles that correlate the acoustic indices with real biological data are represented by an orange line and studies that did not correlate acoustic indices with such data are shown with a green line.

Data extraction

Main extracted data

For each study:

  • We retrieved the acoustic diversity indices calculated and related to biological diversity.
  • We classified the diversity estimates that were measured and related to acoustic indices into five types: Species Abundance, number of individuals of a single or several species; Species richness, number of vocal and/or non-vocal species; Species diversity, including more complex diversity indices that also consider species abundance (i.e., Shannon index, Simpson index or species evenness); Abundance of sounds, number of sounds by identified or not identified species; Diversity of sounds, number of sounds types by identified or not identified species.
  • We described the method applied to obtain such biological information based on two variables: Acoustic, data extracted from audio recordings; Non-acoustic, data extracted from sources other than recordings, i.e., literature, field surveys, etc.
  • We included the environment type: Terrestrial and Aquatic.

Handling of pseudoreplication

To account for differences in sampling effort among studies and to support the detection of cases of pseudoreplication (i.e., inadequate specification of the number of independent observations when applying statistical analysis) that might potentially lead to biased statistical tests, we extensively assessed the sampling design and statistics of each study. We identified eight features that summarized both spatial and temporal sampling and statistical tests of all the selected articles. When a mismatch between sample size and the number of true replicates was identified, we classified the analysis as using inflated replication and used true replicates as our sample size for meta-analysis (see Methods- Sampling design and pseudoreplication in the manuscript).

Feature extraction

Table S3 - List of the 34 features used to characterise studies that tested the relationship between acoustic indices and diversity metrics.

feature_rows <- c(5, 4, 4, 4, 6, 11)
categories <- c("Publication", "Biological data", "Acoustic data", "Recording",
                "Sampling design", "Statistics")
features <- c("Authors", "Title", "Journal", "Year of publication", "Peer reviewed",
              "Environment", "Taxonomic group", "Diversity metric", "Diversity source",
              "Acoustic index", "Frequency range", "FFT size", "Noise treatment",
              "Sampling rate", "Audio format", "Recording length", "Recording method",
              "Study sites", "Distance between sites", "Recorders per site", "Recording days",
                "Daily period", "Daily sample",
              "Statistical test", "Independence", "R${^2}$", "r", "b", "t-statistic",
                "Standard error", "Sample size", "Pseudoreplication", "Pseudoreplication type",
                "Adjusted sample size")
descript_publ <- c("", "", "", "", "Whether the study was subjected to peer review (Yes or No)")
descript_bio_data <- c("Ecosystem type where recordings were collected (aquatic or terrestrial)",
                       "Primary studied group (invertebrates, fish, anurans, mammals, birds, or several)",
                       "Species abundance, species richness, species diversity, abundance of sounds, or diversity of sounds",
                       "Method applied to obtain the diversity metric (acoustic or non-acoustic)")
descript_acous_data <- c("ACI, AEI, ADI, AR, BIO, H, Ht, Hf, M, NP, or NSDI",
                         "Range (in Hz) used for index calculation",
                         "Window size of the Fast Fourier Transformation (FFT)",
                         "Audio pre-processing related to noise (noise filtering, noise addition, or exclusion of noisy recordings)")
descript_rec <- c("Number of audio samples per second (in kHz) used for index calculation",
                  "Format of audio files (.wav, .mp3, etc.)",
                  "Length of each recording (in seconds) used for index calculation",
                  "Non-programmed (continuous), programmed (periodic) or manual (by an operator)")
descript_sampling <- c("Number of study sites (= spatial replicates)",
                       "Minimum distance between study sites (in meters)",
                       "Number of recording units per study site",
                       "Number of recording days per study site (= temporal replicates)",
                       "Period recorded within the day (dawn, morning, midday, evening, dusk, night, or all day)",
                       "Number of recordings collected within a day per study site")
descript_stats <- c("Statistical analysis used to test the relationship between acoustic indices and diversity metrics",
                    "Whether the statistical test was considered independent from other tests of the same study ",
                    "Coefficient of determination (for regression analysis)",
                    "Correlation coefficient (for Pearson or Spearman correlation)",
                    "Regression coefficient (for linear regression analysis)",
                    "Statistic value for Student’s t-test",
                    "Standard error of the test coefficient",
                    "Number of observations included in the statistical test",
                    "Inadequate specification of the number of true replicates in the statistical test (Yes or No)",
                    "Spatial, temporal, or spatial-temporal pseudoreplication",
                    "Suitable specification of the number of true replicates (for pseudoreplicated studies)")
descriptions <- c(descript_publ, descript_bio_data, descript_acous_data,
                  descript_rec, descript_sampling, descript_stats)
features_tbl <- data.frame(Category = rep(categories, feature_rows),
                           Features = features,
                           Description = descriptions)
kbl(features_tbl, format = "html") %>%
   #kable_styling(latex_options = c("scale_down", "HOLD_position")) %>%
   row_spec(0, font_size = 16, bold = TRUE) %>%
   column_spec(1, bold = TRUE) %>%
   column_spec(2, extra_css = 'vertical-align: top !important;') %>%
   row_spec(c(1:5, 10:13, 18:23), background = "#eeeeee") %>%
   collapse_rows(columns = 1, valign = "top") %>%
   kable_classic()
Category Features Description
Publication Authors
Title
Journal
Year of publication
Peer reviewed Whether the study was subjected to peer review (Yes or No)
Biological data Environment Ecosystem type where recordings were collected (aquatic or terrestrial)
Taxonomic group Primary studied group (invertebrates, fish, anurans, mammals, birds, or several)
Diversity metric Species abundance, species richness, species diversity, abundance of sounds, or diversity of sounds
Diversity source Method applied to obtain the diversity metric (acoustic or non-acoustic)
Acoustic data Acoustic index ACI, AEI, ADI, AR, BIO, H, Ht, Hf, M, NP, or NSDI
Frequency range Range (in Hz) used for index calculation
FFT size Window size of the Fast Fourier Transformation (FFT)
Noise treatment Audio pre-processing related to noise (noise filtering, noise addition, or exclusion of noisy recordings)
Recording Sampling rate Number of audio samples per second (in kHz) used for index calculation
Audio format Format of audio files (.wav, .mp3, etc.)
Recording length Length of each recording (in seconds) used for index calculation
Recording method Non-programmed (continuous), programmed (periodic) or manual (by an operator)
Sampling design Study sites Number of study sites (= spatial replicates)
Distance between sites Minimum distance between study sites (in meters)
Recorders per site Number of recording units per study site
Recording days Number of recording days per study site (= temporal replicates)
Daily period Period recorded within the day (dawn, morning, midday, evening, dusk, night, or all day)
Daily sample Number of recordings collected within a day per study site
Statistics Statistical test Statistical analysis used to test the relationship between acoustic indices and diversity metrics
Independence Whether the statistical test was considered independent from other tests of the same study
R\({^2}\) Coefficient of determination (for regression analysis)
r Correlation coefficient (for Pearson or Spearman correlation)
b Regression coefficient (for linear regression analysis)
t-statistic Statistic value for Student’s t-test
Standard error Standard error of the test coefficient
Sample size Number of observations included in the statistical test
Pseudoreplication Inadequate specification of the number of true replicates in the statistical test (Yes or No)
Pseudoreplication type Spatial, temporal, or spatial-temporal pseudoreplication
Adjusted sample size Suitable specification of the number of true replicates (for pseudoreplicated studies)

Effect size calculation

We used Pearson’s correlation coefficient (r), as our measure of effect size. The effect size describes the direction and magnitude of the relationship between acoustic indices and diversity metrics.

When Pearson’s correlation, r, was not reported, we extracted other statistics, using the following precedence: Spearman’s correlation, t-values, F-values, linear regression slope coefficients and R². When only graphical information was available, we extracted the statistics with Web Plot Digitizer v4.2. (Rohatgi, 2019) and computed Pearson correlations. We converted all statistics to r, using compute.es package in R (Del Re & Del Re, 2012) or when the package did not provide the needed functions we followed the formulas provided in Nakagawa & Cuthill (2007) and Koricheva, Gurevitch, & Mengersen (2013).

We converted our effect size r to Fisher’s Z in order to satisfy the normality assumption of parametric meta-analysis (Nakagawa & Cuthill, 2007). Fisher’s Z values were converted back to r, to ease interpretation of results.

Data set

We collected a total of 481 effect sizes from 35 studies. For the meta-analysis, these number was reduced to 364 effect sizes and 34 studies, after computing composite effect sizes between non-independent effect sizes and removing a study due to difficulty in describing the study design. For the literature review, we kept the original 35 studies.

    df_raw <- read.csv("data/Table.S1.csv")
    n_used <- "n_adjusted"
    # Use n_adjusted as sample size
    df_tidy <- tidy_data(df_raw, n_used)
## Removed study id
##   54 
## Dataframe aggregated from 481 to 364 entries
    # Studies database
    studies <- read.csv("data/Table.S2.csv")
    df_tidy <- merge(df_tidy, studies, by.x = "id", by.y = "ID", all.x = TRUE)
    df_tidy <- df_tidy %>% 
               mutate(authors = paste(Authors, year)) %>%
               select(id, entry, authors, everything(), -Publ_year, -Title,
                      -doi, -Authors)

Table S1 - Data set used in the study.

kable(df_tidy, format = "html") %>%
     kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
     scroll_box(height = "400px", width = "100%")
id entry authors year impact_factor index taxa environ bio diversity_source pseudoreplication n z var Journal
2 15 Desjonquères et al. 2015 2015 2.180 ACI invertebrates A sound_abundance acoustic YES 4 0.3285000 0.7500000 PeerJ
2 16 Desjonquères et al. 2015 2015 2.180 ACI invertebrates A sound_richness acoustic YES 4 0.3322500 0.7500000 PeerJ
2 17 Desjonquères et al. 2015 2015 2.180 AR invertebrates A sound_abundance acoustic YES 4 0.0692000 0.7500000 PeerJ
2 18 Desjonquères et al. 2015 2015 2.180 AR invertebrates A sound_richness acoustic YES 4 0.0851500 0.7500000 PeerJ
2 19 Desjonquères et al. 2015 2015 2.180 Hf invertebrates A sound_abundance acoustic YES 4 -0.1851000 0.7500000 PeerJ
2 20 Desjonquères et al. 2015 2015 2.180 Hf invertebrates A sound_richness acoustic YES 4 -0.1304000 0.7500000 PeerJ
2 21 Desjonquères et al. 2015 2015 2.180 Ht invertebrates A sound_abundance acoustic YES 4 -0.3286500 0.7500000 PeerJ
2 22 Desjonquères et al. 2015 2015 2.180 Ht invertebrates A sound_richness acoustic YES 4 -0.2970000 0.7500000 PeerJ
2 23 Desjonquères et al. 2015 2015 2.180 M invertebrates A sound_abundance acoustic YES 4 0.3453000 0.7500000 PeerJ
2 24 Desjonquères et al. 2015 2015 2.180 M invertebrates A sound_richness acoustic YES 4 0.3043500 0.7500000 PeerJ
2 25 Desjonquères et al. 2015 2015 2.180 NP invertebrates A sound_abundance acoustic YES 4 0.2074500 0.7500000 PeerJ
2 26 Desjonquères et al. 2015 2015 2.180 NP invertebrates A sound_richness acoustic YES 4 0.1851000 0.7500000 PeerJ
4 13 Parks et al. 2014 2014 1.730 H mammals A sound_abundance acoustic YES 4 0.2801000 0.7500000 Ecol. Inform.
6 1 Boelman et al. 2007 2007 3.570 BIO birds T abundance no_acoustic NO 8 1.5047000 0.2000000 Ecol. Apli.
9 44 Harris et al. 2016 2016 5.710 ACI fish A diversity no_acoustic NO 9 1.0278500 0.1250250 Methods Ecol. Evol.
9 45 Harris et al. 2016 2016 5.710 ACI fish A richness no_acoustic NO 9 0.2877000 0.1667000 Methods Ecol. Evol.
9 46 Harris et al. 2016 2016 5.710 AR fish A diversity no_acoustic NO 9 0.2104000 0.1250250 Methods Ecol. Evol.
9 47 Harris et al. 2016 2016 5.710 AR fish A richness no_acoustic NO 9 0.5361000 0.1667000 Methods Ecol. Evol.
9 48 Harris et al. 2016 2016 5.710 H fish A diversity no_acoustic NO 9 0.3258625 0.0937688 Methods Ecol. Evol.
9 49 Harris et al. 2016 2016 5.710 H fish A richness no_acoustic NO 9 0.6448500 0.1041875 Methods Ecol. Evol.
10 38 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.1003000 0.0303000 Sci Rep
10 39 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.0000000 0.0303000 Sci Rep
10 170 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.8291000 0.0303000 Sci Rep
10 171 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.1717000 0.0303000 Sci Rep
10 228 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 1.2562000 0.0303000 Sci Rep
10 229 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.2132000 0.0303000 Sci Rep
10 276 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.9076000 0.0303000 Sci Rep
10 277 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.1923000 0.0303000 Sci Rep
10 300 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.1206000 0.0303000 Sci Rep
10 301 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.2554000 0.0303000 Sci Rep
10 311 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.0500000 0.0303000 Sci Rep
10 312 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.2769000 0.0303000 Sci Rep
10 326 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.3428000 0.0303000 Sci Rep
10 327 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.8673000 0.0303000 Sci Rep
10 339 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.3205000 0.0303000 Sci Rep
10 340 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 1.0454000 0.0303000 Sci Rep
10 350 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.3769000 0.0303000 Sci Rep
10 351 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 1.0203000 0.0303000 Sci Rep
10 361 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.2237000 0.0303000 Sci Rep
10 362 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.9287000 0.0303000 Sci Rep
10 363 Buscaino et al. 2016 2016 4.259 ACI fish A sound_abundance acoustic NO 36 0.3428000 0.0303000 Sci Rep
10 364 Buscaino et al. 2016 2016 4.259 ACI invertebrates A sound_abundance acoustic NO 36 0.7582000 0.0303000 Sci Rep
11 40 Bertucci et al. 2016 2016 4.260 ACI fish A abundance no_acoustic NO 8 -0.1262000 0.1500000 Sci Rep
11 41 Bertucci et al. 2016 2016 4.260 ACI fish A diversity no_acoustic NO 8 1.0328000 0.2000000 Sci Rep
11 42 Bertucci et al. 2016 2016 4.260 ACI fish A richness no_acoustic NO 8 0.8821000 0.1500000 Sci Rep
11 172 Bertucci et al. 2016 2016 4.260 ACI fish A abundance no_acoustic NO 8 0.3009000 0.1500000 Sci Rep
11 173 Bertucci et al. 2016 2016 4.260 ACI fish A diversity no_acoustic NO 8 0.4181000 0.2000000 Sci Rep
11 174 Bertucci et al. 2016 2016 4.260 ACI fish A richness no_acoustic NO 8 1.1245500 0.1500000 Sci Rep
11 230 Bertucci et al. 2016 2016 4.260 ACI fish A abundance no_acoustic NO 8 -0.1797000 0.1500000 Sci Rep
11 231 Bertucci et al. 2016 2016 4.260 ACI fish A diversity no_acoustic NO 8 0.8429000 0.2000000 Sci Rep
11 232 Bertucci et al. 2016 2016 4.260 ACI fish A richness no_acoustic NO 8 0.6372500 0.1500000 Sci Rep
11 278 Bertucci et al. 2016 2016 4.260 ACI fish A abundance no_acoustic NO 8 -0.0471500 0.1500000 Sci Rep
11 279 Bertucci et al. 2016 2016 4.260 ACI fish A diversity no_acoustic NO 8 0.6747000 0.2000000 Sci Rep
11 280 Bertucci et al. 2016 2016 4.260 ACI fish A richness no_acoustic NO 8 0.6980000 0.1500000 Sci Rep
13 11 McWilliam & Hawkin 2013 2013 2.480 ACI invertebrates A sound_abundance acoustic YES 5 1.1881000 0.5000000 J. ExpMar. Biol. Ecol
13 12 McWilliam & Hawkin 2013 2013 2.480 ADI invertebrates A sound_abundance acoustic YES 5 1.3331000 0.5000000 J. ExpMar. Biol. Ecol
14 34 Wa Maina et al. 2016 2016 1.220 ACI birds T richness acoustic NO 8 0.1689000 0.2000000 BDJ
14 35 Wa Maina et al. 2016 2016 1.220 ACI birds T richness no_acoustic NO 8 0.9638000 0.2000000 BDJ
14 36 Wa Maina et al. 2016 2016 1.220 H birds T richness acoustic NO 8 0.4676000 0.2000000 BDJ
14 37 Wa Maina et al. 2016 2016 1.220 H birds T richness no_acoustic NO 8 0.5870000 0.2000000 BDJ
15 43 Roca & Proulx 2016 2016 4.810 H invertebrates T richness acoustic NO 4 1.8972000 1.0000000 Ecology
15 175 Roca & Proulx 2016 2016 4.810 H invertebrates T richness acoustic NO 4 2.6467000 1.0000000 Ecology
15 233 Roca & Proulx 2016 2016 4.810 H invertebrates T richness acoustic NO 4 2.3796000 1.0000000 Ecology
17 14 Zhang et al. 2015 2015 0.000 ACI birds T richness acoustic YES 4 0.3940000 1.0000000 IEEE.Conference.procedings
37 33 Picciulin et al. 2016 2016 0.000 ACI fish A sound_abundance acoustic YES 4 0.3260000 1.0000000 Proceedings of Meetings on Acoustics
41 3 Paisley-Jones 2011 2011 0.000 H birds T sound_abundance acoustic NO 6 0.1481000 0.3333000 thesis
41 4 Paisley-Jones 2011 2011 0.000 H invertebrates T diversity no_acoustic NO 6 -0.1430000 0.3333000 thesis
44 86 Machado et al. 2017 2017 4.994 ADI birds T richness acoustic NO 30 0.4910000 0.0370000 Landsc. Urban Plan.
44 87 Machado et al. 2017 2017 4.994 NDSI birds T richness acoustic NO 30 0.1686000 0.0370000 Landsc. Urban Plan.
45 7 McLaren 2012 2012 0.000 NDSI birds T diversity no_acoustic NO 36 0.9330000 0.0303000 practicum
45 8 McLaren 2012 2012 0.000 NDSI birds T richness acoustic NO 36 0.6070000 0.0303000 practicum
45 9 McLaren 2012 2012 0.000 NDSI birds T richness no_acoustic NO 36 0.9160000 0.0303000 practicum
53 154 Moreno-Gomez 2019 2019 4.490 ACI anurans T richness acoustic NO 33 0.0000000 0.0333000 Ecol. Indic.
53 155 Moreno-Gomez 2019 2019 4.490 ACI birds T richness acoustic NO 33 -0.0209000 0.0333000 Ecol. Indic.
53 156 Moreno-Gomez 2019 2019 4.490 ADI anurans T richness acoustic NO 33 0.1051000 0.0333000 Ecol. Indic.
53 157 Moreno-Gomez 2019 2019 4.490 ADI birds T richness acoustic NO 33 -0.3237000 0.0333000 Ecol. Indic.
53 158 Moreno-Gomez 2019 2019 4.490 AEI anurans T richness acoustic NO 33 -0.2122000 0.0333000 Ecol. Indic.
53 159 Moreno-Gomez 2019 2019 4.490 AEI birds T richness acoustic NO 33 0.3237000 0.0333000 Ecol. Indic.
53 160 Moreno-Gomez 2019 2019 4.490 BIO anurans T richness acoustic NO 33 0.1051000 0.0333000 Ecol. Indic.
53 161 Moreno-Gomez 2019 2019 4.490 BIO birds T richness acoustic NO 33 -0.1051000 0.0333000 Ecol. Indic.
53 162 Moreno-Gomez 2019 2019 4.490 H anurans T richness acoustic NO 33 0.1051000 0.0333000 Ecol. Indic.
53 163 Moreno-Gomez 2019 2019 4.490 H birds T richness acoustic NO 33 -0.1051000 0.0333000 Ecol. Indic.
53 164 Moreno-Gomez 2019 2019 4.490 Hf anurans T richness acoustic NO 33 -0.1051000 0.0333000 Ecol. Indic.
53 165 Moreno-Gomez 2019 2019 4.490 Hf birds T richness acoustic NO 33 0.1051000 0.0333000 Ecol. Indic.
53 166 Moreno-Gomez 2019 2019 4.490 Ht anurans T richness acoustic NO 33 0.1051000 0.0333000 Ecol. Indic.
53 167 Moreno-Gomez 2019 2019 4.490 Ht birds T richness acoustic NO 33 -0.3237000 0.0333000 Ecol. Indic.
53 214 Moreno-Gomez 2019 2019 4.490 ACI anurans T richness acoustic NO 11 -0.1051000 0.1250000 Ecol. Indic.
53 215 Moreno-Gomez 2019 2019 4.490 ACI birds T richness acoustic NO 11 0.3237000 0.1250000 Ecol. Indic.
53 216 Moreno-Gomez 2019 2019 4.490 ADI anurans T richness acoustic NO 11 0.1051000 0.1250000 Ecol. Indic.
53 217 Moreno-Gomez 2019 2019 4.490 ADI birds T richness acoustic NO 11 -0.2122000 0.1250000 Ecol. Indic.
53 218 Moreno-Gomez 2019 2019 4.490 AEI anurans T richness acoustic NO 11 -0.1051000 0.1250000 Ecol. Indic.
53 219 Moreno-Gomez 2019 2019 4.490 AEI birds T richness acoustic NO 11 0.3237000 0.1250000 Ecol. Indic.
53 220 Moreno-Gomez 2019 2019 4.490 BIO anurans T richness acoustic NO 11 -0.1051000 0.1250000 Ecol. Indic.
53 221 Moreno-Gomez 2019 2019 4.490 BIO birds T richness acoustic NO 11 0.2122000 0.1250000 Ecol. Indic.
53 222 Moreno-Gomez 2019 2019 4.490 H anurans T richness acoustic NO 11 0.1051000 0.1250000 Ecol. Indic.
53 223 Moreno-Gomez 2019 2019 4.490 H birds T richness acoustic NO 11 -0.3237000 0.1250000 Ecol. Indic.
53 224 Moreno-Gomez 2019 2019 4.490 Hf anurans T richness acoustic NO 11 -0.0105000 0.1250000 Ecol. Indic.
53 225 Moreno-Gomez 2019 2019 4.490 Hf birds T richness acoustic NO 11 -0.1051000 0.1250000 Ecol. Indic.
53 226 Moreno-Gomez 2019 2019 4.490 Ht anurans T richness acoustic NO 11 0.1051000 0.1250000 Ecol. Indic.
53 227 Moreno-Gomez 2019 2019 4.490 Ht birds T richness acoustic NO 11 -0.4426000 0.1250000 Ecol. Indic.
53 262 Moreno-Gomez 2019 2019 4.490 ACI anurans T richness acoustic NO 32 0.1051000 0.0345000 Ecol. Indic.
53 263 Moreno-Gomez 2019 2019 4.490 ACI birds T richness acoustic NO 32 0.5731000 0.0345000 Ecol. Indic.
53 264 Moreno-Gomez 2019 2019 4.490 ADI anurans T richness acoustic NO 32 0.1051000 0.0345000 Ecol. Indic.
53 265 Moreno-Gomez 2019 2019 4.490 ADI birds T richness acoustic NO 32 -0.1051000 0.0345000 Ecol. Indic.
53 266 Moreno-Gomez 2019 2019 4.490 AEI anurans T richness acoustic NO 32 -0.1051000 0.0345000 Ecol. Indic.
53 267 Moreno-Gomez 2019 2019 4.490 AEI birds T richness acoustic NO 32 0.2122000 0.0345000 Ecol. Indic.
53 268 Moreno-Gomez 2019 2019 4.490 BIO anurans T richness acoustic NO 32 -0.2122000 0.0345000 Ecol. Indic.
53 269 Moreno-Gomez 2019 2019 4.490 BIO birds T richness acoustic NO 32 0.3237000 0.0345000 Ecol. Indic.
53 270 Moreno-Gomez 2019 2019 4.490 H anurans T richness acoustic NO 32 0.0105000 0.0345000 Ecol. Indic.
53 271 Moreno-Gomez 2019 2019 4.490 H birds T richness acoustic NO 32 0.3237000 0.0345000 Ecol. Indic.
53 272 Moreno-Gomez 2019 2019 4.490 Hf anurans T richness acoustic NO 32 0.0000000 0.0345000 Ecol. Indic.
53 273 Moreno-Gomez 2019 2019 4.490 Hf birds T richness acoustic NO 32 0.3237000 0.0345000 Ecol. Indic.
53 274 Moreno-Gomez 2019 2019 4.490 Ht anurans T richness acoustic NO 32 0.0105000 0.0345000 Ecol. Indic.
53 275 Moreno-Gomez 2019 2019 4.490 Ht birds T richness acoustic NO 32 -0.4426000 0.0345000 Ecol. Indic.
60 152 Patrick Lyon et al. 2019 2019 2.360 ACI fish A abundance no_acoustic NO 7 -0.3654000 0.2500000 Mar. Ecol.-Prog. Ser.
60 153 Patrick Lyon et al. 2019 2019 2.360 ACI fish A diversity no_acoustic NO 7 0.1306000 0.1875000 Mar. Ecol.-Prog. Ser.
60 212 Patrick Lyon et al. 2019 2019 2.360 ACI fish A abundance no_acoustic NO 7 0.5763000 0.2500000 Mar. Ecol.-Prog. Ser.
60 213 Patrick Lyon et al. 2019 2019 2.360 ACI fish A diversity no_acoustic NO 7 0.4313500 0.1875000 Mar. Ecol.-Prog. Ser.
70 94 Bolgan et al. 2018 2018 4.010 ACI fish A sound_abundance acoustic NO 9 -0.2232000 0.1250250 Sci Rep
70 95 Bolgan et al. 2018 2018 4.010 ACI fish A sound_richness acoustic NO 9 0.6011000 0.1667000 Sci Rep
70 199 Bolgan et al. 2018 2018 4.010 ACI fish A sound_abundance acoustic NO 4 0.0262000 1.0000000 Sci Rep
70 200 Bolgan et al. 2018 2018 4.010 ACI fish A sound_abundance acoustic NO 9 0.6011000 0.1667000 Sci Rep
70 201 Bolgan et al. 2018 2018 4.010 ACI fish A sound_abundance acoustic NO 10 0.4676000 0.1429000 Sci Rep
70 202 Bolgan et al. 2018 2018 4.010 ACI fish A sound_richness acoustic NO 9 0.1051000 0.1667000 Sci Rep
70 203 Bolgan et al. 2018 2018 4.010 ACI fish A sound_richness acoustic NO 10 0.4931000 0.1429000 Sci Rep
70 256 Bolgan et al. 2018 2018 4.010 ACI fish A sound_abundance acoustic NO 9 0.8244000 0.1667000 Sci Rep
70 257 Bolgan et al. 2018 2018 4.010 ACI fish A sound_richness acoustic NO 9 1.2973000 0.1667000 Sci Rep
70 294 Bolgan et al. 2018 2018 4.010 ACI fish A sound_abundance acoustic YES 25 0.3422714 0.0260000 Sci Rep
70 295 Bolgan et al. 2018 2018 4.010 ACI fish A sound_richness acoustic YES 25 0.2930143 0.0260000 Sci Rep
77 71 Fairbrass et al. 2017 2017 3.980 ACI several T richness acoustic NO 105 0.8513000 0.0099000 Ecol. Indic.
77 80 Fairbrass et al. 2017 2017 3.980 BIO several T richness acoustic NO 105 0.4545000 0.0099000 Ecol. Indic.
77 85 Fairbrass et al. 2017 2017 3.980 NDSI several T richness acoustic NO 105 0.4320000 0.0099000 Ecol. Indic.
77 186 Fairbrass et al. 2017 2017 3.980 ADI several T richness acoustic NO 105 0.1968000 0.0099000 Ecol. Indic.
80 62 Staaterman et al. 2017 2017 2.280 ACI fish A abundance no_acoustic NO 4 -0.1203000 0.6666667 Mar. Ecol.-Prog. Ser.
80 63 Staaterman et al. 2017 2017 2.280 ACI fish A diversity no_acoustic NO 4 0.0000000 0.7500000 Mar. Ecol.-Prog. Ser.
80 64 Staaterman et al. 2017 2017 2.280 ACI fish A richness no_acoustic NO 4 -0.2158000 0.6666667 Mar. Ecol.-Prog. Ser.
80 65 Staaterman et al. 2017 2017 2.280 H fish A abundance no_acoustic NO 4 0.4060667 0.6666667 Mar. Ecol.-Prog. Ser.
80 66 Staaterman et al. 2017 2017 2.280 H fish A diversity no_acoustic NO 4 -0.1586500 0.7500000 Mar. Ecol.-Prog. Ser.
80 67 Staaterman et al. 2017 2017 2.280 H fish A richness no_acoustic NO 4 0.3178667 0.6666667 Mar. Ecol.-Prog. Ser.
80 176 Staaterman et al. 2017 2017 2.280 ACI fish A abundance no_acoustic NO 4 0.0335667 0.6666667 Mar. Ecol.-Prog. Ser.
80 177 Staaterman et al. 2017 2017 2.280 ACI fish A diversity no_acoustic NO 4 0.1990500 0.7500000 Mar. Ecol.-Prog. Ser.
80 178 Staaterman et al. 2017 2017 2.280 ACI fish A richness no_acoustic NO 4 0.0357000 0.6666667 Mar. Ecol.-Prog. Ser.
80 179 Staaterman et al. 2017 2017 2.280 H fish A abundance no_acoustic NO 4 0.4564000 0.6666667 Mar. Ecol.-Prog. Ser.
80 180 Staaterman et al. 2017 2017 2.280 H fish A diversity no_acoustic NO 4 -0.2144000 0.7500000 Mar. Ecol.-Prog. Ser.
80 181 Staaterman et al. 2017 2017 2.280 H fish A richness no_acoustic NO 4 0.4316333 0.6666667 Mar. Ecol.-Prog. Ser.
80 234 Staaterman et al. 2017 2017 2.280 ACI fish A abundance no_acoustic NO 4 0.0357000 0.6666667 Mar. Ecol.-Prog. Ser.
80 235 Staaterman et al. 2017 2017 2.280 ACI fish A diversity no_acoustic NO 4 -0.2144000 0.7500000 Mar. Ecol.-Prog. Ser.
80 236 Staaterman et al. 2017 2017 2.280 ACI fish A richness no_acoustic NO 4 -0.3736000 0.6666667 Mar. Ecol.-Prog. Ser.
80 237 Staaterman et al. 2017 2017 2.280 H fish A abundance no_acoustic NO 4 0.2231333 0.6666667 Mar. Ecol.-Prog. Ser.
80 238 Staaterman et al. 2017 2017 2.280 H fish A diversity no_acoustic NO 4 0.1093000 0.7500000 Mar. Ecol.-Prog. Ser.
80 239 Staaterman et al. 2017 2017 2.280 H fish A richness no_acoustic NO 4 0.0417667 0.6666667 Mar. Ecol.-Prog. Ser.
80 281 Staaterman et al. 2017 2017 2.280 ACI fish A abundance no_acoustic NO 4 0.0357000 0.6666667 Mar. Ecol.-Prog. Ser.
80 282 Staaterman et al. 2017 2017 2.280 ACI fish A diversity no_acoustic NO 4 -0.1586500 0.7500000 Mar. Ecol.-Prog. Ser.
80 283 Staaterman et al. 2017 2017 2.280 ACI fish A richness no_acoustic NO 4 -0.3385667 0.6666667 Mar. Ecol.-Prog. Ser.
80 284 Staaterman et al. 2017 2017 2.280 H fish A abundance no_acoustic NO 4 0.2231333 0.6666667 Mar. Ecol.-Prog. Ser.
80 285 Staaterman et al. 2017 2017 2.280 H fish A diversity no_acoustic NO 4 -0.1061000 0.7500000 Mar. Ecol.-Prog. Ser.
80 286 Staaterman et al. 2017 2017 2.280 H fish A richness no_acoustic NO 4 0.2055667 0.6666667 Mar. Ecol.-Prog. Ser.
86 92 Indraswari et al. 2018 2018 3.400 ACI anurans T sound_abundance acoustic YES 33 0.6284000 0.0333000 Freshw. Biol.
86 93 Indraswari et al. 2018 2018 3.400 Ht anurans T sound_abundance acoustic YES 33 0.6948000 0.0333000 Freshw. Biol.
86 197 Indraswari et al. 2018 2018 3.400 ACI anurans T sound_abundance acoustic YES 33 0.3773000 0.0333000 Freshw. Biol.
86 198 Indraswari et al. 2018 2018 3.400 Ht anurans T sound_abundance acoustic YES 33 0.4426000 0.0333000 Freshw. Biol.
86 254 Indraswari et al. 2018 2018 3.400 ACI anurans T sound_abundance acoustic YES 33 0.2617000 0.0333000 Freshw. Biol.
86 255 Indraswari et al. 2018 2018 3.400 Ht anurans T sound_abundance acoustic YES 33 0.4880000 0.0333000 Freshw. Biol.
87 96 Eldridge et al. 2018 2018 4.490 ACI birds T richness acoustic YES 4 0.6250000 0.6000000 Ecol. Indic.
87 98 Eldridge et al. 2018 2018 4.490 ACI birds T sound_abundance acoustic YES 4 0.6507600 0.6000000 Ecol. Indic.
87 204 Eldridge et al. 2018 2018 4.490 ACI birds T richness acoustic YES 4 0.1909000 0.6000000 Ecol. Indic.
87 206 Eldridge et al. 2018 2018 4.490 ACI several T sound_abundance acoustic YES 4 -0.0819200 0.6000000 Ecol. Indic.
87 258 Eldridge et al. 2018 2018 4.490 ADI birds T richness acoustic YES 4 -0.7218000 1.0000000 Ecol. Indic.
87 259 Eldridge et al. 2018 2018 4.490 ADI birds T sound_abundance acoustic YES 4 -0.7547000 1.0000000 Ecol. Indic.
87 260 Eldridge et al. 2018 2018 4.490 AEI birds T richness acoustic YES 4 0.5061000 1.0000000 Ecol. Indic.
87 261 Eldridge et al. 2018 2018 4.490 AEI birds T sound_abundance acoustic YES 4 0.8429000 1.0000000 Ecol. Indic.
87 296 Eldridge et al. 2018 2018 4.490 ADI birds T richness acoustic YES 4 -0.2078000 1.0000000 Ecol. Indic.
87 297 Eldridge et al. 2018 2018 4.490 ADI several T sound_abundance acoustic YES 4 -0.5061000 1.0000000 Ecol. Indic.
87 298 Eldridge et al. 2018 2018 4.490 AEI birds T richness acoustic YES 4 0.2100000 1.0000000 Ecol. Indic.
87 299 Eldridge et al. 2018 2018 4.490 AEI several T sound_abundance acoustic YES 4 0.5731000 1.0000000 Ecol. Indic.
87 309 Eldridge et al. 2018 2018 4.490 BIO birds T richness acoustic YES 4 0.8064000 1.0000000 Ecol. Indic.
87 310 Eldridge et al. 2018 2018 4.490 BIO birds T sound_abundance acoustic YES 4 0.9009000 1.0000000 Ecol. Indic.
87 320 Eldridge et al. 2018 2018 4.490 BIO birds T richness acoustic YES 4 0.2100000 1.0000000 Ecol. Indic.
87 321 Eldridge et al. 2018 2018 4.490 BIO several T sound_abundance acoustic YES 4 0.6446000 1.0000000 Ecol. Indic.
87 322 Eldridge et al. 2018 2018 4.490 Hf birds T richness acoustic YES 4 -0.5731000 1.0000000 Ecol. Indic.
87 323 Eldridge et al. 2018 2018 4.490 Hf birds T sound_abundance acoustic YES 4 -0.7218000 1.0000000 Ecol. Indic.
87 324 Eldridge et al. 2018 2018 4.490 Ht birds T richness acoustic YES 4 -0.6595000 1.0000000 Ecol. Indic.
87 325 Eldridge et al. 2018 2018 4.490 Ht birds T sound_abundance acoustic YES 4 -0.6902000 1.0000000 Ecol. Indic.
87 335 Eldridge et al. 2018 2018 4.490 Hf birds T richness acoustic YES 4 -0.1905000 1.0000000 Ecol. Indic.
87 336 Eldridge et al. 2018 2018 4.490 Hf several T sound_abundance acoustic YES 4 -0.6446000 1.0000000 Ecol. Indic.
87 337 Eldridge et al. 2018 2018 4.490 Ht birds T richness acoustic YES 4 -0.1905000 1.0000000 Ecol. Indic.
87 338 Eldridge et al. 2018 2018 4.490 Ht several T sound_abundance acoustic YES 4 -0.3237000 1.0000000 Ecol. Indic.
87 348 Eldridge et al. 2018 2018 4.490 NDSI birds T richness acoustic YES 4 0.3237000 1.0000000 Ecol. Indic.
87 349 Eldridge et al. 2018 2018 4.490 NDSI birds T sound_abundance acoustic YES 4 0.4303000 1.0000000 Ecol. Indic.
87 359 Eldridge et al. 2018 2018 4.490 NDSI birds T richness acoustic YES 4 0.2672000 1.0000000 Ecol. Indic.
87 360 Eldridge et al. 2018 2018 4.490 NDSI several T sound_abundance acoustic YES 4 0.4303000 1.0000000 Ecol. Indic.
89 50 Gage et al. 2017 2017 1.820 ACI birds T richness acoustic YES 60 0.6885000 0.0175000 Ecol. Inform.
89 51 Gage et al. 2017 2017 1.820 ACI birds T sound_abundance acoustic YES 60 1.4618000 0.0175000 Ecol. Inform.
89 52 Gage et al. 2017 2017 1.820 ADI birds T richness acoustic YES 60 -1.8635000 0.0175000 Ecol. Inform.
89 53 Gage et al. 2017 2017 1.820 ADI birds T sound_abundance acoustic YES 60 -0.8852000 0.0175000 Ecol. Inform.
89 54 Gage et al. 2017 2017 1.820 AEI birds T richness acoustic YES 60 2.2494000 0.0175000 Ecol. Inform.
89 55 Gage et al. 2017 2017 1.820 AEI birds T sound_abundance acoustic YES 60 1.0302000 0.0175000 Ecol. Inform.
89 56 Gage et al. 2017 2017 1.820 BIO birds T richness acoustic YES 60 -0.6098000 0.0175000 Ecol. Inform.
89 57 Gage et al. 2017 2017 1.820 BIO birds T sound_abundance acoustic YES 60 -0.0993000 0.0175000 Ecol. Inform.
89 58 Gage et al. 2017 2017 1.820 H birds T richness acoustic YES 60 1.0849000 0.0175000 Ecol. Inform.
89 59 Gage et al. 2017 2017 1.820 H birds T sound_abundance acoustic YES 60 2.4427000 0.0175000 Ecol. Inform.
89 60 Gage et al. 2017 2017 1.820 NDSI birds T richness acoustic YES 60 0.0270000 0.0175000 Ecol. Inform.
89 61 Gage et al. 2017 2017 1.820 NDSI birds T sound_abundance acoustic YES 60 0.5269000 0.0175000 Ecol. Inform.
90 104 Ferreira et al. 2018 2018 NA ACI anurans T sound_richness acoustic NO 7 0.0556000 0.2500000 Journal of ecoacoustics
90 108 Ferreira et al. 2018 2018 NA ACI birds T sound_richness acoustic NO 7 -0.1125000 0.2500000 Journal of ecoacoustics
90 109 Ferreira et al. 2018 2018 NA ACI invertebrates T sound_richness acoustic NO 7 0.1765000 0.2500000 Journal of ecoacoustics
90 110 Ferreira et al. 2018 2018 NA ACI mammals T sound_richness acoustic NO 7 -0.0598000 0.2500000 Journal of ecoacoustics
90 111 Ferreira et al. 2018 2018 NA ADI anurans T sound_richness acoustic NO 7 1.0277000 0.2500000 Journal of ecoacoustics
90 115 Ferreira et al. 2018 2018 NA ADI birds T sound_richness acoustic NO 7 -0.4747000 0.2500000 Journal of ecoacoustics
90 116 Ferreira et al. 2018 2018 NA ADI invertebrates T sound_richness acoustic NO 7 0.8162000 0.2500000 Journal of ecoacoustics
90 117 Ferreira et al. 2018 2018 NA ADI mammals T sound_richness acoustic NO 7 0.3826000 0.2500000 Journal of ecoacoustics
90 118 Ferreira et al. 2018 2018 NA AEI anurans T sound_richness acoustic NO 7 -0.9660000 0.2500000 Journal of ecoacoustics
90 122 Ferreira et al. 2018 2018 NA AEI birds T sound_richness acoustic NO 7 0.6011000 0.2500000 Journal of ecoacoustics
90 123 Ferreira et al. 2018 2018 NA AEI invertebrates T sound_richness acoustic NO 7 -0.7058000 0.2500000 Journal of ecoacoustics
90 124 Ferreira et al. 2018 2018 NA AEI mammals T sound_richness acoustic NO 7 -0.4389000 0.2500000 Journal of ecoacoustics
90 125 Ferreira et al. 2018 2018 NA BIO anurans T sound_richness acoustic NO 7 -0.1743000 0.2500000 Journal of ecoacoustics
90 129 Ferreira et al. 2018 2018 NA BIO birds T sound_richness acoustic NO 7 -0.1358000 0.2500000 Journal of ecoacoustics
90 130 Ferreira et al. 2018 2018 NA BIO invertebrates T sound_richness acoustic NO 7 -0.0556000 0.2500000 Journal of ecoacoustics
90 131 Ferreira et al. 2018 2018 NA BIO mammals T sound_richness acoustic NO 7 -0.0839000 0.2500000 Journal of ecoacoustics
90 132 Ferreira et al. 2018 2018 NA H anurans T sound_richness acoustic NO 7 1.2090000 0.2500000 Journal of ecoacoustics
90 136 Ferreira et al. 2018 2018 NA H birds T sound_richness acoustic NO 7 -0.5048000 0.2500000 Journal of ecoacoustics
90 137 Ferreira et al. 2018 2018 NA H invertebrates T sound_richness acoustic NO 7 0.8949000 0.2500000 Journal of ecoacoustics
90 138 Ferreira et al. 2018 2018 NA H mammals T sound_richness acoustic NO 7 0.4169000 0.2500000 Journal of ecoacoustics
90 142 Ferreira et al. 2018 2018 NA NDSI anurans T sound_richness acoustic NO 7 1.1933000 0.2500000 Journal of ecoacoustics
90 146 Ferreira et al. 2018 2018 NA NDSI birds T sound_richness acoustic NO 7 -0.5567000 0.2500000 Journal of ecoacoustics
90 147 Ferreira et al. 2018 2018 NA NDSI invertebrates T sound_richness acoustic NO 7 0.8485000 0.2500000 Journal of ecoacoustics
90 148 Ferreira et al. 2018 2018 NA NDSI mammals T sound_richness acoustic NO 7 0.4513000 0.2500000 Journal of ecoacoustics
92 88 Torti et al. 2018 2018 1.950 ACI mammals T abundance no_acoustic NO 258 0.6150000 0.0039000
92 89 Torti et al. 2018 2018 1.950 ADI mammals T abundance no_acoustic NO 258 0.0174000 0.0039000
92 90 Torti et al. 2018 2018 1.950 AR mammals T abundance no_acoustic NO 258 0.0266000 0.0039000
92 91 Torti et al. 2018 2018 1.950 H mammals T abundance no_acoustic NO 258 0.1404000 0.0039000
96 105 Izaguirre et al. 2018 2018 NA ACI birds T abundance no_acoustic YES 12 0.7315000 0.1111000 Journal of ecoacoustics
96 106 Izaguirre et al. 2018 2018 NA ACI birds T diversity no_acoustic YES 12 -0.7068500 0.0833250 Journal of ecoacoustics
96 107 Izaguirre et al. 2018 2018 NA ACI birds T richness no_acoustic YES 12 -0.7941000 0.1111000 Journal of ecoacoustics
96 112 Izaguirre et al. 2018 2018 NA ADI birds T abundance no_acoustic YES 12 -0.7958000 0.1111000 Journal of ecoacoustics
96 113 Izaguirre et al. 2018 2018 NA ADI birds T diversity no_acoustic YES 12 0.6084000 0.0833250 Journal of ecoacoustics
96 114 Izaguirre et al. 2018 2018 NA ADI birds T richness no_acoustic YES 12 0.5192000 0.1111000 Journal of ecoacoustics
96 119 Izaguirre et al. 2018 2018 NA AEI birds T abundance no_acoustic YES 12 0.7430000 0.1111000 Journal of ecoacoustics
96 120 Izaguirre et al. 2018 2018 NA AEI birds T diversity no_acoustic YES 12 -0.5394000 0.0833250 Journal of ecoacoustics
96 121 Izaguirre et al. 2018 2018 NA AEI birds T richness no_acoustic YES 12 -0.4181000 0.1111000 Journal of ecoacoustics
96 126 Izaguirre et al. 2018 2018 NA BIO birds T abundance no_acoustic YES 12 0.5553000 0.1111000 Journal of ecoacoustics
96 127 Izaguirre et al. 2018 2018 NA BIO birds T diversity no_acoustic YES 12 -0.6801500 0.0833250 Journal of ecoacoustics
96 128 Izaguirre et al. 2018 2018 NA BIO birds T richness no_acoustic YES 12 -0.5485000 0.1111000 Journal of ecoacoustics
96 133 Izaguirre et al. 2018 2018 NA H birds T abundance no_acoustic YES 12 -0.3904000 0.1111000 Journal of ecoacoustics
96 134 Izaguirre et al. 2018 2018 NA H birds T diversity no_acoustic YES 12 0.3747000 0.0833250 Journal of ecoacoustics
96 135 Izaguirre et al. 2018 2018 NA H birds T richness no_acoustic YES 12 0.5773000 0.1111000 Journal of ecoacoustics
96 139 Izaguirre et al. 2018 2018 NA M birds T abundance no_acoustic YES 12 0.4944000 0.1111000 Journal of ecoacoustics
96 140 Izaguirre et al. 2018 2018 NA M birds T diversity no_acoustic YES 12 -0.6604000 0.0833250 Journal of ecoacoustics
96 141 Izaguirre et al. 2018 2018 NA M birds T richness no_acoustic YES 12 -0.6686000 0.1111000 Journal of ecoacoustics
96 143 Izaguirre et al. 2018 2018 NA NDSI birds T abundance no_acoustic YES 12 -0.0955000 0.1111000 Journal of ecoacoustics
96 144 Izaguirre et al. 2018 2018 NA NDSI birds T diversity no_acoustic YES 12 0.2976500 0.0833250 Journal of ecoacoustics
96 145 Izaguirre et al. 2018 2018 NA NDSI birds T richness no_acoustic YES 12 0.4012000 0.1111000 Journal of ecoacoustics
96 149 Izaguirre et al. 2018 2018 NA NP birds T abundance no_acoustic YES 12 -0.5983000 0.1111000 Journal of ecoacoustics
96 150 Izaguirre et al. 2018 2018 NA NP birds T diversity no_acoustic YES 12 0.6247500 0.0833250 Journal of ecoacoustics
96 151 Izaguirre et al. 2018 2018 NA NP birds T richness no_acoustic YES 12 0.6372000 0.1111000 Journal of ecoacoustics
251 168 Buxton et al. 2016 2016 2.440 ACI birds T diversity acoustic NO 72 0.2300000 0.0108750 Ecol. Evol.
251 169 Buxton et al. 2016 2016 2.440 ACI birds T richness acoustic NO 72 0.3673000 0.0145000 Ecol. Evol.
427 27 Fuller et al. 2015 2015 3.190 ACI birds T richness acoustic NO 380 0.0503000 0.0027000 Ecol. Indic.
427 28 Fuller et al. 2015 2015 3.190 ADI birds T richness acoustic NO 380 0.0395000 0.0027000 Ecol. Indic.
427 29 Fuller et al. 2015 2015 3.190 AEI birds T richness acoustic NO 380 0.0864000 0.0027000 Ecol. Indic.
427 30 Fuller et al. 2015 2015 3.190 BIO birds T richness acoustic NO 380 0.0543000 0.0027000 Ecol. Indic.
427 31 Fuller et al. 2015 2015 3.190 H birds T richness acoustic NO 380 0.1281000 0.0027000 Ecol. Indic.
427 32 Fuller et al. 2015 2015 3.190 NDSI birds T richness acoustic NO 380 0.1517000 0.0027000 Ecol. Indic.
1132 10 Depraetere et al. 2012 2012 2.890 AR birds T richness acoustic NO 12 1.7295000 0.1111000 Ecol. Indic.
1177 5 Joo et al. 2011 2011 2.170 H birds T richness acoustic YES 5 0.1820000 0.5000000 Landsc. Urban Plan.
1262 6 Pieretti et al. 2011 2011 2.700 ACI birds T sound_abundance acoustic YES 20 1.2568600 0.0352800 Ecol. Indic.
2740 69 Mammides et al. 2017 2017 3.980 ACI birds T diversity no_acoustic NO 47 0.1614000 0.0227000 Ecol. Indic.
2740 70 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 47 0.2132000 0.0227000 Ecol. Indic.
2740 72 Mammides et al. 2017 2017 3.980 ADI birds T diversity no_acoustic NO 47 0.3769000 0.0227000 Ecol. Indic.
2740 73 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 47 0.3654000 0.0227000 Ecol. Indic.
2740 74 Mammides et al. 2017 2017 3.980 AEI birds T diversity no_acoustic NO 47 -0.4118000 0.0227000 Ecol. Indic.
2740 75 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 47 -0.4236000 0.0227000 Ecol. Indic.
2740 76 Mammides et al. 2017 2017 3.980 AR birds T diversity no_acoustic NO 47 -0.4847000 0.0227000 Ecol. Indic.
2740 77 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 47 -0.4973000 0.0227000 Ecol. Indic.
2740 78 Mammides et al. 2017 2017 3.980 BIO birds T diversity no_acoustic NO 47 -0.2986000 0.0227000 Ecol. Indic.
2740 79 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 47 -0.3541000 0.0227000 Ecol. Indic.
2740 81 Mammides et al. 2017 2017 3.980 H birds T diversity no_acoustic NO 47 0.5361000 0.0227000 Ecol. Indic.
2740 82 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 47 0.5627000 0.0227000 Ecol. Indic.
2740 83 Mammides et al. 2017 2017 3.980 NDSI birds T diversity no_acoustic NO 47 -0.0100000 0.0227000 Ecol. Indic.
2740 84 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 47 -0.0100000 0.0227000 Ecol. Indic.
2740 182 Mammides et al. 2017 2017 3.980 ACI birds T diversity no_acoustic NO 47 0.0601000 0.0227000 Ecol. Indic.
2740 183 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 47 -0.0300000 0.0227000 Ecol. Indic.
2740 184 Mammides et al. 2017 2017 3.980 ADI birds T diversity no_acoustic NO 47 0.7250000 0.0227000 Ecol. Indic.
2740 185 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 47 0.6184000 0.0227000 Ecol. Indic.
2740 187 Mammides et al. 2017 2017 3.980 AEI birds T diversity no_acoustic NO 47 -0.7089000 0.0227000 Ecol. Indic.
2740 188 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 47 -0.6042000 0.0227000 Ecol. Indic.
2740 189 Mammides et al. 2017 2017 3.980 AR birds T diversity no_acoustic NO 47 -0.2448000 0.0227000 Ecol. Indic.
2740 190 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 47 -0.2448000 0.0227000 Ecol. Indic.
2740 191 Mammides et al. 2017 2017 3.980 BIO birds T diversity no_acoustic NO 47 0.2027000 0.0227000 Ecol. Indic.
2740 192 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 47 0.2342000 0.0227000 Ecol. Indic.
2740 193 Mammides et al. 2017 2017 3.980 H birds T diversity no_acoustic NO 47 0.7928000 0.0227000 Ecol. Indic.
2740 194 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 47 0.6777000 0.0227000 Ecol. Indic.
2740 195 Mammides et al. 2017 2017 3.980 NDSI birds T diversity no_acoustic NO 47 0.6931000 0.0227000 Ecol. Indic.
2740 196 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 47 0.6042000 0.0227000 Ecol. Indic.
2740 240 Mammides et al. 2017 2017 3.980 ACI birds T diversity no_acoustic NO 50 0.0601000 0.0213000 Ecol. Indic.
2740 241 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 50 0.0400000 0.0213000 Ecol. Indic.
2740 242 Mammides et al. 2017 2017 3.980 ADI birds T diversity no_acoustic NO 50 0.5101000 0.0213000 Ecol. Indic.
2740 243 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 50 0.6328000 0.0213000 Ecol. Indic.
2740 244 Mammides et al. 2017 2017 3.980 AEI birds T diversity no_acoustic NO 50 -0.4973000 0.0213000 Ecol. Indic.
2740 245 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 50 -0.6475000 0.0213000 Ecol. Indic.
2740 246 Mammides et al. 2017 2017 3.980 AR birds T diversity no_acoustic NO 50 -0.1003000 0.0213000 Ecol. Indic.
2740 247 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 50 -0.0802000 0.0213000 Ecol. Indic.
2740 248 Mammides et al. 2017 2017 3.980 BIO birds T diversity no_acoustic NO 50 0.2237000 0.0213000 Ecol. Indic.
2740 249 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 50 0.3884000 0.0213000 Ecol. Indic.
2740 250 Mammides et al. 2017 2017 3.980 H birds T diversity no_acoustic NO 50 0.3095000 0.0213000 Ecol. Indic.
2740 251 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 50 0.3769000 0.0213000 Ecol. Indic.
2740 252 Mammides et al. 2017 2017 3.980 NDSI birds T diversity no_acoustic NO 50 0.2769000 0.0213000 Ecol. Indic.
2740 253 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 50 0.3654000 0.0213000 Ecol. Indic.
2740 287 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 10 0.0701000 0.1429000 Ecol. Indic.
2740 288 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 10 0.6042000 0.1429000 Ecol. Indic.
2740 289 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 10 -0.6625000 0.1429000 Ecol. Indic.
2740 290 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 10 -0.1003000 0.1429000 Ecol. Indic.
2740 291 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 10 0.1923000 0.1429000 Ecol. Indic.
2740 292 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 10 0.4236000 0.1429000 Ecol. Indic.
2740 293 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 10 0.3769000 0.1429000 Ecol. Indic.
2740 302 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 10 0.0300000 0.1429000 Ecol. Indic.
2740 303 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 10 0.6475000 0.1429000 Ecol. Indic.
2740 304 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 10 -0.6777000 0.1429000 Ecol. Indic.
2740 305 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 10 -0.1003000 0.1429000 Ecol. Indic.
2740 306 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 10 0.2237000 0.1429000 Ecol. Indic.
2740 307 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 10 0.4722000 0.1429000 Ecol. Indic.
2740 308 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 10 0.4847000 0.1429000 Ecol. Indic.
2740 313 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 10 0.0701000 0.1429000 Ecol. Indic.
2740 314 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 10 0.6184000 0.1429000 Ecol. Indic.
2740 315 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 10 -0.6777000 0.1429000 Ecol. Indic.
2740 316 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 10 -0.0601000 0.1429000 Ecol. Indic.
2740 317 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 10 0.2237000 0.1429000 Ecol. Indic.
2740 318 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 10 0.4118000 0.1429000 Ecol. Indic.
2740 319 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 10 0.3316000 0.1429000 Ecol. Indic.
2740 328 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 10 0.0300000 0.1429000 Ecol. Indic.
2740 329 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 10 0.6184000 0.1429000 Ecol. Indic.
2740 330 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 10 -0.6777000 0.1429000 Ecol. Indic.
2740 331 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 10 -0.0601000 0.1429000 Ecol. Indic.
2740 332 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 10 0.2342000 0.1429000 Ecol. Indic.
2740 333 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 10 0.4236000 0.1429000 Ecol. Indic.
2740 334 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 10 0.3884000 0.1429000 Ecol. Indic.
2740 341 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 10 0.1104000 0.1429000 Ecol. Indic.
2740 342 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 10 0.6042000 0.1429000 Ecol. Indic.
2740 343 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 10 -0.6475000 0.1429000 Ecol. Indic.
2740 344 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 10 -0.0701000 0.1429000 Ecol. Indic.
2740 345 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 10 0.2342000 0.1429000 Ecol. Indic.
2740 346 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 10 0.4236000 0.1429000 Ecol. Indic.
2740 347 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 10 0.4477000 0.1429000 Ecol. Indic.
2740 352 Mammides et al. 2017 2017 3.980 ACI birds T richness no_acoustic NO 10 0.0902000 0.1429000 Ecol. Indic.
2740 353 Mammides et al. 2017 2017 3.980 ADI birds T richness no_acoustic NO 10 0.6328000 0.1429000 Ecol. Indic.
2740 354 Mammides et al. 2017 2017 3.980 AEI birds T richness no_acoustic NO 10 -0.6777000 0.1429000 Ecol. Indic.
2740 355 Mammides et al. 2017 2017 3.980 AR birds T richness no_acoustic NO 10 -0.1003000 0.1429000 Ecol. Indic.
2740 356 Mammides et al. 2017 2017 3.980 BIO birds T richness no_acoustic NO 10 0.2027000 0.1429000 Ecol. Indic.
2740 357 Mammides et al. 2017 2017 3.980 H birds T richness no_acoustic NO 10 0.4599000 0.1429000 Ecol. Indic.
2740 358 Mammides et al. 2017 2017 3.980 NDSI birds T richness no_acoustic NO 10 0.4356000 0.1429000 Ecol. Indic.
2745 2 Sueur et al. 2008 2008 4.810 H several T richness acoustic NO 10 1.7211000 0.1429000 PLoS One
2977 97 Jorge et al. 2018 2018 4.490 ACI birds T richness acoustic YES 9 0.5731000 0.1667000 Ecol. Indic.
2977 99 Jorge et al. 2018 2018 4.490 ADI birds T richness acoustic YES 9 -0.2672000 0.1667000 Ecol. Indic.
2977 100 Jorge et al. 2018 2018 4.490 AEI birds T richness acoustic YES 9 0.3123000 0.1667000 Ecol. Indic.
2977 101 Jorge et al. 2018 2018 4.490 BIO birds T richness acoustic YES 9 0.4181000 0.1667000 Ecol. Indic.
2977 102 Jorge et al. 2018 2018 4.490 H birds T richness acoustic YES 9 -0.1475000 0.1667000 Ecol. Indic.
2977 103 Jorge et al. 2018 2018 4.490 NDSI birds T richness acoustic YES 9 0.3352000 0.1667000 Ecol. Indic.
2977 205 Jorge et al. 2018 2018 4.490 ACI birds T richness acoustic YES 9 0.3123000 0.1667000 Ecol. Indic.
2977 207 Jorge et al. 2018 2018 4.490 ADI birds T richness acoustic YES 9 -0.4676000 0.1667000 Ecol. Indic.
2977 208 Jorge et al. 2018 2018 4.490 AEI birds T richness acoustic YES 9 0.5192000 0.1667000 Ecol. Indic.
2977 209 Jorge et al. 2018 2018 4.490 BIO birds T richness acoustic YES 9 0.3009000 0.1667000 Ecol. Indic.
2977 210 Jorge et al. 2018 2018 4.490 H birds T richness acoustic YES 9 -0.1582000 0.1667000 Ecol. Indic.
2977 211 Jorge et al. 2018 2018 4.490 NDSI birds T richness acoustic YES 9 0.3352000 0.1667000 Ecol. Indic.
2986 68 Raynor et al. 2017 2017 2.720 ACI birds T richness acoustic YES 6 0.2448000 0.3333000 Condor


Table S2 - Variable descriptions for Table S1.

vd <- data.frame(Variables = c("id", "entry", "year", "impact_factor", "index",
                               "taxa", "environ", "bio", "diversity_source", "pseudoreplication",
                               "n", "z", "var", "journal"),
                 Descriptions = c(
                      "Identification number for the study",
                      "Identification number for the effect size (row entry)",
                      "Year of publication",  
                      "Impact factor of the Journal",
                      "Acoustic index used",
                      "Studied taxonomic group studied  (invertebrates, fish, anurans, mammals, birds or several)",
                      "Studied environment (i.e., ecosystem type where recordings were collected: *aquatic* [A] or *terrestrial* [T]",
                      "Diversity metric used to correlate with acoustic index values",  
                      paste("Method applied to obtain the diversity metric: audio recordings (*acoustic*) or other sources (field surveys, literature, etc.; *non-acoustic*)"),
                      paste("Inadequate specification of the number of observations in the statistical test (Yes/No)"),  
                      "Adjusted sample size (i.e., suitable specification of the number of true replicates)",
                      "Fisher's Z effect size",
                      "Fisher's Z variance",
                      "Journal where the study was published"
             ) 
           )
pander(vd, justify = "left")
Variables Descriptions
id Identification number for the study
entry Identification number for the effect size (row entry)
year Year of publication
impact_factor Impact factor of the Journal
index Acoustic index used
taxa Studied taxonomic group studied (invertebrates, fish, anurans, mammals, birds or several)
environ Studied environment (i.e., ecosystem type where recordings were collected: aquatic [A] or terrestrial [T]
bio Diversity metric used to correlate with acoustic index values
diversity_source Method applied to obtain the diversity metric: audio recordings (acoustic) or other sources (field surveys, literature, etc.; non-acoustic)
pseudoreplication Inadequate specification of the number of observations in the statistical test (Yes/No)
n Adjusted sample size (i.e., suitable specification of the number of true replicates)
z Fisher’s Z effect size
var Fisher’s Z variance
journal Journal where the study was published

Data set overview

In what follows, we provide an overview of the data set used in the meta-analysis.

Our data set comprised a total of 34 studies and 364 effect sizes. Therefore, most studies contributed with more than one effect size for the meta-analysis.

Table S4 - Number of effect sizes collected from each of the 34 studies included in the meta-analysis. ID corresponds to the study identification number in our data set.

studies_n <- as.data.frame(table(df_tidy$id), stringsAsFactors = FALSE)
studies_n <- cbind(unique(df_tidy$authors), studies_n)
colnames(studies_n) <- c("Study", "ID", "Effect_sizes")
studies_n$Study <- author_format(studies_n$Study, markup = "markdown")

studies_n <- studies_n %>%
              select(ID, Study, Effect_sizes) %>%
              arrange(-Effect_sizes)

kable(studies_n, format = "html") %>%
  kable_styling(c("striped"))
ID Study Effect_sizes
2740 Mammides et al. (2017) 84
53 Moreno-Gómez et al. (2019) 42
87 Eldridge et al. (2018) 28
80 Staaterman et al. (2017) 24
90 Ferreira et al. (2018) 24
96 Retamosa Izaguirre & Ramírez-Alán (2018) 24
10 Buscaino et al. (2016) 22
2 Desjonquères et al. (2015) 12
11 Bertucci et al. (2016) 12
89 Gage et al. (2017) 12
2977 Jorge et al. (2018) 12
70 Bolgan et al. (2018) 11
9 Harris et al. (2016) 6
86 Indraswari et al. (2018) 6
427 Fuller et al. (2015) 6
14 wa Maina et al. (2016) 4
60 Lyon et al. (2019) 4
77 Fairbrass et al. (2017) 4
92 Torti et al. (2018) 4
15 Roca & Proulx (2016) 3
45 McLaren & DeGroote (2012) 3
13 McWilliam & Hawkins (2013) 2
41 Paisley-Jones (2011) 2
44 Machado et al. (2017) 2
251 Buxton et al. (2016) 2
4 Parks et al. (2014) 1
6 Boelman et al. (2007) 1
17 Zhang et al. (2015) 1
37 Picciulin et al. (2016) 1
1132 Depraetere et al. (2012) 1
1177 Joo et al. (2011) 1
1262 Pieretti et al. (2011) 1
2745 Sueur et al. (2008) 1
2986 Raynor et al. (2017) 1


The most studied acoustic index was ACI and the most used biodiversity metric was species richness.

Table S5 - Number of effect sizes and studies per moderator level.

# Table for moderator levels
mods <- c("index", "bio", "diversity_source", "environ")
sample_sizes <- do.call(rbind, lapply(mods, function(x) n_studies_entries(df_tidy, x)))

# Format output
sample_sizes <- cbind(row.names(sample_sizes), sample_sizes)
sample_sizes <- as.data.frame(sample_sizes)
names(sample_sizes) <- c("Moderator_levels", "Effect_sizes", "Studies")
sample_sizes$Moderator_levels <- final_mod_names(sample_sizes$Moderator_levels)
sample_sizes$Moderator_levels <- str_replace(sample_sizes$Moderator_levels, "^([a-z])", toupper)
n_row <- nrow(sample_sizes)
sample_sizes$Moderator_levels[(n_row - 1):n_row] <- c("Aquatic", "Terrestrial")

names(sample_sizes) <- str_replace(names(sample_sizes), "_", " ")
kable(sample_sizes, format = "html", row.names = FALSE) %>%
    kable_styling("striped", full_width = FALSE, position = "left") %>%
    row_spec(0, font_size = 16, bold = TRUE) %>%
    pack_rows("Acoustic indices", 1, 11) %>%
    pack_rows("Biodiversity metrics", 12, 16) %>%
    pack_rows("Diversity source", 17, 18) %>%
    pack_rows("Environment", 19, 20)
Moderator levels Effect sizes Studies
Acoustic indices
ACI 113 25
ADI 38 12
AEI 34 8
AR 18 5
BIO 36 10
H 55 16
Hf 12 3
Ht 15 4
M 5 2
NDSI 33 10
NP 5 2
Biodiversity metrics
Species abundance 27 6
Species diversity 49 9
Richness 187 21
Abundance of sounds 66 11
Diversity of sounds 35 3
Diversity source
Acoustic 200 26
Non acoustic 164 11
Environment
Aquatic 95 10
Terrestrial 269 24

Visual description of eligible studies

We gathered studies from 6 of the 7 continents (no studies in Antarctica). Most studies were conducted in the USA, France, Australia and Brazil.

knitr::include_graphics("rmd/mapa.png")

Figure 5A - The geographic distribution of the study sites corresponding to the 35 studies used in the literature review. The colouring of countries exhibits a white to black gradient relative to an increase in the number of studies contributed by each country. The coloured dots discriminate between different groups of studied taxa.


The performance of acoustic diversity indices as a biodiversity indicators was mainly assessed with species richness and abundance of sounds as diversity metrics, mainly on birds.

knitr::include_graphics("rmd/heatmap.png")

Figure 5B - Distribution of the number of articles by diversity metric, taxa and acoustic index studied, from the 35 studies included in the literature review.


A large set of studies (40%) exhibited statistical deficiencies in testing the relationship between acoustic indices and diversity metrics due to pseudoreplication.

knitr::include_graphics("rmd/ps.index.png")

Figure S1 - Pseudoreplication summary.

Meta-analysis

We clustered effect sizes within their corresponding studies and conducted multilevel meta-analysis using Fisher’s Z as our response variable. The multilevel structure accounted for the correlation structures within studies and thus allowed the inclusion of multiple effect sizes per study.

Summary effect size

We tested whether acoustic indices were good estimators of biodiversity by computing an intercept-only model. The resulting summary effect size not only gives a clue of whether acoustic indices are performing well in estimating biodiversity across the literature, but also allows us to check if there is substantial heterogeneity in our effect sizes that could be explained by moderators.

Intercept-only meta-analysis output 
res_main <- rma.mv(z, var, random = ~1 | id/entry, data = df_tidy)
res_main
## 
## Multivariate Meta-Analysis Model (k = 364; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0458  0.2139     34     no        id 
## sigma^2.2  0.1755  0.4190    364     no  id/entry 
## 
## Test for Heterogeneity:
## Q(df = 363) = 2220.9097, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub     ​ 
##   0.3461  0.0577  6.0014  <.0001  0.2331  0.4591  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Table S7 - Resulting estimates from intercept-only model converted to Pearson’s correlation. “Estimate” is the Pearson’s r summary effect size. “CI.lb” and “CI.ub” are the confidence intervals lower and upper bounds, respectively.

r_main <- sapply(c(r = res_main$b, CI.lb = res_main$ci.lb, CI.ub = res_main$ci.ub), z2r)
r_main_pander <- r_main
names(r_main_pander) <- c("Estimate", names(r_main)[2:3])
pander(r_main_pander)
Estimate CI.lb CI.ub
0.3329 0.2289 0.4294

The summary estimate indicates that acoustic indices showed a moderate correlation with diversity metrics. However, this result is an overall estimate and thus can hide differences in performance between acoustic indices or context-dependencies due to, for example, different environments or diversity metrics. For this, we would need to inspect moderators, but before it is necessary to check before if our intercept-only model has unexplained variance that can be partitioned by our chosen moderators.

Effect sizes in ascending order

The amount of heterogeneity in effect sizes can be coarsely inspected by plotting all effect sizes and respective variances, and see their dispersion along the x-axis.

df_all_plt <- mutate(df_tidy, z = z2r(z), var = z2r(var))
r_main <- as.data.frame(t(r_main))
nudge <- 2
overall_y <- -nudge - 1
ggplot(data = df_all_plt, aes(x = z, y = reorder(entry, -z))) + 
    geom_errorbarh(aes(xmin = z - 1.96 * var, xmax = z + 1.96 * var),
                   height = 0, size = 0.5, color = "grey", 
                   position = position_nudge(y = nudge)) + 
    geom_point(size = 0.8, color = "darkgreen", 
               position = position_nudge(y = nudge)) +
    geom_segment(aes(x = z2r(res_main$b), y = overall_y, 
                     xend = z2r(res_main$b), 
                     yend = nrow(df_all_plt) + nudge + 10), 
                     color = alpha("forestgreen", 0.7), linetype = 2, size = 0.5) +   
    geom_vline(xintercept = 0, linetype = 1) +
    # Insert overall estimate
    geom_errorbarh(aes(xmin = r_main$CI.lb, xmax = r_main$CI.ub, y = overall_y), 
                   color = "grey") +
    geom_point(data = r_main, aes(x = r, y = overall_y), size = 3, color = "forestgreen") +
    geom_hline(yintercept = nudge - 1, color = alpha("black", 0.5), linetype = 5, size = 1) + 
    annotate("text", x = -1.4, y = overall_y, label= "Overall estimate", size = 4, adj = "right") + 
    scale_y_discrete(expand = c(0.025, 0.01)) +
    xlab("Effect size (r)") +
    ylab("Data set entries ordered by effect size magnitude") +
    theme_minimal() + 
    theme(axis.text.x = element_text(size = 12, color = "black"),
          axis.text.y = element_blank(),
          axis.line.x = element_line(color = "black"),
          axis.line.y = element_line(color = "black"),
          axis.title = element_text(size = 14),
          panel.grid.major.y = element_blank(), 
          legend.position = "none"
    )

Figure 6A - Pearson correlation effect sizes (r) in ascending order of magnitude from all data set entries. Effect sizes larger than 0 (vertical line) represent a positive correlation between acoustic indices and diversity. Effect sizes below 0 indicate a negative correlation between acoustic indices and diversity. Above the dashed horizontal line, the green circles are effect sizes means, with corresponding 95% confidence intervals (grey horizontal lines). Below the dashed line, the green circle is the overall effect size, with a corresponding 95% confidence interval, obtained from the intercept-only meta-analysis.

Check heterogeneity

We quantified heterogeneity with the I² statistic, which estimates the proportion of unknown variation in effect sizes not attributed to sampling error variance.


Web Supplementary Table 1 - Unaccounted heterogeneity of the intercept-only model as measured by I2 statistic. Within study heterogeneity (level 2) corresponds to the unaccounted variation that is found on effect sizes within studies, and between study heterogeneity corresponds to the unaccounted variation between studies (level 3).

font_css <- "font-family: Arial"
Is <- multilevel_I(res_main) * 100
Is_df <- data.frame(Is[1], Is[2])
names(Is_df) <- c("Within study", "Between study")
rownames(Is_df) <- c("% Unexplained variation")
total_I <- paste("Total heterogeneity: ", round(Is[1] + Is[2], 2), "%")
header <- c(3)
names(header) <- c(total_I)
kable(Is_df, format = "html", digits = 2) %>%
  kable_styling(c("striped", "bordered"), full_width = FALSE, 
                position = "center") %>%
  add_header_above(header, font_size = 16, bold = TRUE,
                   extra_css = font_css) %>%
  row_spec(0, font_size = 14, extra_css = font_css) %>%
  row_spec(1, font_size = 12, align = "center")
Total heterogeneity: 85.13 %
Within study Between study
% Unexplained variation 17.61 67.52 
mlm.variance.distribution(res_main)

Figure S2 - Visual representation of how variance was distributed over the multilevel structure of the intercept-only model. Within study heterogeneity (level 2) corresponds to the unaccounted variation that is found on effect sizes within studies, and between study heterogeneity corresponds to the unaccounted variation between studies (level 3).


The value of I2 = 85% corresponds to the amount of heterogeneity that remains unaccounted for in the intercept-only model, and gives a green signal to proceed with the use of moderators that can potentially explain some of this variation.

Analysis of moderators

We extended the previous intercept-only model with the inclusion of moderators as fixed factors. For these analyses, all moderator levels with less than 5 studies were excluded as low study sizes are more liable to produce biased estimates. This led to the removal of the acoustic indices ‘Hf’, ‘Ht’, ‘M’ and ‘NP’, and the diversity metric ‘diversity of sounds’ from model fitting procedures.

Sub-group analysis

We conducted sub-group analysis with acoustic index as a moderator to assess which acoustic indices best correlate with biodiversity.

To specifically test whether the effect size estimates from each acoustic index were different from zero we removed the model intercept.

df_indices <-  clear_moderators(df_tidy, "index")
## Levels dropped from dataframe:
##  Moderator index
##       Hf Ht M NP
Meta-analysis output with acoustic indices as moderator and no intercept. 
res_indices <- rma.mv(z, var, random = ~ 1 | id/entry, mods = ~ index - 1, data = df_indices)
res_indices
## 
## Multivariate Meta-Analysis Model (k = 327; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0295  0.1716     34     no        id 
## sigma^2.2  0.1710  0.4135    327     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 320) = 1876.0325, p-val < .0001
## 
## Test of Moderators (coefficients 1:7):
## QM(df = 7) = 70.5454, p-val < .0001
## 
## Model Results:
## 
##            estimate      se    zval    pval    ci.lb   ci.ub     ​ 
## indexACI     0.3809  0.0685  5.5596  <.0001   0.2466  0.5152  *** 
## indexADI     0.2493  0.0977  2.5506  0.0108   0.0577  0.4408    * 
## indexAEI     0.0396  0.1048  0.3774  0.7059  -0.1658  0.2449      
## indexAR      0.0780  0.1354  0.5756  0.5649  -0.1875  0.3434      
## indexBIO     0.1950  0.1012  1.9266  0.0540  -0.0034  0.3934    . 
## indexH       0.5511  0.0903  6.1036  <.0001   0.3742  0.7281  *** 
## indexNDSI    0.4557  0.1037  4.3944  <.0001   0.2524  0.6589  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Table S8 - Resulting estimates from sub-group analysis. The ‘Estimate’ column is the Pearson correlation effect size; SE is the standard error and CI.lb and CI.up; and the lower and upper bounds of the confidence intervals, respectively.

df_pred_indices <- get_predictions(res_indices, intercept = FALSE)
df_pred_indices$coef <- str_remove(df_pred_indices$coef, "index")
names(df_pred_indices) <- c("Index", "Estimate", "SE", "CI.lb", "CI.ub")
pander(df_pred_indices)
Index Estimate SE CI.lb CI.ub
ACI 0.363 0.068 0.242 0.474
ADI 0.244 0.097 0.058 0.414
AEI 0.04 0.104 -0.164 0.24
AR 0.078 0.135 -0.185 0.331
BIO 0.193 0.101 -0.003 0.374
H 0.501 0.09 0.358 0.622
NDSI 0.427 0.103 0.247 0.578 
nentries_index <- rowSums(table(df_indices$index, df_indices$id))
nstudies_index <- rowSums(ifelse(table(df_indices$index, df_indices$id) > 0, 1, 0))
n_index <- paste0(nentries_index, " (", nstudies_index, ")")

df_indices_plt <- data.frame("index" = names(nstudies_index),
                             "es" = z2r(res_indices$beta), 
                             "se" = z2r(res_indices$se), 
                             "ci.lb" = z2r(res_indices$ci.lb),
                             "ci.ub" = z2r(res_indices$ci.ub),
                             "n" = nstudies_index)

ggplot(data = df_indices_plt, aes(x = es, y = index)) + 
    geom_point(aes(color = index), size = 4) +
    geom_errorbarh(aes(xmin = ci.lb, xmax = ci.ub, color= index),
                   height = 0) + 
    geom_vline(xintercept = 0, linetype = 1) +
    geom_vline(xintercept = z2r(res_main$b), color = "forestgreen", 
               linetype = 2) + 
    scale_y_discrete(limits = rev(df_indices_plt$index)) +
    scale_color_brewer(palette="Dark2") +
    theme_minimal() + 
    xlab("Effect size (r)") +
    theme(axis.text.x = element_text(size = 12, color = "black"),
          axis.text.y = element_text(size = 13, color = "black"),
          axis.line.x = element_line(color = "black"),
          axis.line.y = element_line(color = "black"),
          axis.title.x = element_text(size = 14),
          axis.title.y = element_blank(),
          legend.position = "none"
    )

Figure S3 - Effect size mean estimates (circles) and corresponding 95% confidence intervals (horizontal lines) obtained from sub-group meta-analysis with acoustic indices as the grouping factor. Estimated effect sizes whose 95% confidence intervals do not overlap zero (black vertical line) indicate a positive correlation between acoustic indices and diversity if they are to the right of zero, or a negative correlation if they are to the left of zero. The dashed green vertical line represents the summary effect size obtained from the intercept only meta-analysis.


Meta-regression

We used meta-regression to check the effect of multiple moderators on the ability of acoustic indices to estimate biodiversity. We focused on four moderators that could alter the performance of biodiversity estimation, namely:

  1. Acoustic index - which acoustic index was used to estimate biodiversity.
  2. Diversity metrics – which metric of diversity was used to check the performance of acoustic index.
  3. Environment – if recordings were done in aquatic or terrestrial environments;
  4. Diversity source – if the diversity metric was obtained from acoustic (examination of sound recordings) or non-acoustic sources (e.g., field surveys).

We set as intercept the following combination of moderator levels: ACI index, species richness, terrestrial environment and non-acoustic data source.

Due to low study sample size between most factor level combinations, we were constrained to use only an additive effects model.

mods <- c("index", "bio", "environ", "diversity_source")
df_full <- clear_moderators(df_tidy, mods)
## Levels dropped from dataframe:
##  Moderator index
##       Hf Ht M NP
##  Moderator bio
##       sound_richness
##  Moderator environ
##       
##  Moderator diversity_source
##
Meta-analysis with acoustic indices, biodiversity metrics, environment and diversity source as moderators. 
res_full <- rma.mv(z, var, random = ~1 | id/entry, 
                   mods = ~ index + bio + environ + diversity_source, 
                   data = df_full)
 
res_full
## 
## Multivariate Meta-Analysis Model (k = 296; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0355  0.1884     33     no        id 
## sigma^2.2  0.1738  0.4168    296     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 284) = 1730.2577, p-val < .0001
## 
## Test of Moderators (coefficients 2:12):
## QM(df = 11) = 27.4277, p-val = 0.0040
## 
## Model Results:
## 
##                           estimate      se     zval    pval    ci.lb    ci.ub​ 
## intrcpt                     0.3590  0.1416   2.5359  0.0112   0.0815   0.6365 
## indexADI                   -0.1294  0.1171  -1.1049  0.2692  -0.3590   0.1001 
## indexAEI                   -0.2916  0.1231  -2.3679  0.0179  -0.5329  -0.0502 
## indexAR                    -0.2735  0.1482  -1.8461  0.0649  -0.5639   0.0169 
## indexBIO                   -0.1449  0.1203  -1.2038  0.2287  -0.3807   0.0910 
## indexH                      0.1977  0.1092   1.8111  0.0701  -0.0162   0.4117 
## indexNDSI                   0.0840  0.1260   0.6667  0.5050  -0.1630   0.3310 
## bioabundance               -0.0815  0.1589  -0.5133  0.6078  -0.3929   0.2298 
## biodiversity               -0.0420  0.0950  -0.4423  0.6583  -0.2283   0.1442 
## biosound_abundance          0.2600  0.1470   1.7690  0.0769  -0.0281   0.5480 
## environA                   -0.0656  0.1484  -0.4422  0.6583  -0.3565   0.2252 
## diversity_sourceacoustic   -0.0091  0.1464  -0.0623  0.9504  -0.2962   0.2779 
##  
## intrcpt                   * 
## indexADI 
## indexAEI                  * 
## indexAR                   . 
## indexBIO 
## indexH                    . 
## indexNDSI 
## bioabundance 
## biodiversity 
## biosound_abundance        . 
## environA 
## diversity_sourceacoustic 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Multicollinearity

Multicollinearity among our moderators was inspected with VIF and found not to be an issue in our model (VIF < 1.7 for all moderators, threshold value of 3 (Zuur, Ieno, & Elphick, 2010).

Table S6 - VIF values obtained for each moderator level.

vif_meta <- vif.rma(res_full, table = TRUE)$vif["vif"][-1, , drop = FALSE]
vif_meta <- tibble::rownames_to_column(vif_meta, "Moderators")
colnames(vif_meta)[2] <- "VIF" 
vif_meta$VIF <- round(vif_meta$VIF, 3)
vif_meta$Moderators <- str_remove(vif_meta$Moderators, "index|bio|environ|diversity_source")
vif_meta$Moderators <- final_mod_names(vif_meta$Moderators)
pander(vif_meta)
Moderators VIF
ADI 1.497
AEI 1.45
AR 1.25
BIO 1.478
H 1.472
NDSI 1.437
Species abundance 1.244
Species diversity 1.156
Abundance of sounds 1.454
Aquatic 1.393
Acoustic 1.623


Heterogeneity full model

Substantial heterogeneity remained to be explained after fitting the full model. Therefore, other factors not tested or an interaction among our moderators could have increased the ability to extract even more information from the data set. For the latter, it is important to conduct more studies on the correlation between acoustic indices and biodiversity in order to allow the computation of more complex models.

# Fit overall model with df_full (without levels with less than 5 studies)
res_main_full <- rma.mv(z, var, random = ~1 | id/entry, data = df_full)
sum_I_main_full <- sum(multilevel_I(res_main_full))
sum_I_full <- sum(multilevel_I(res_full))

cat("Heterogenity after fitting the full model\n\t", sum_I_full, "\n\n",
    "Difference heterogeneity between intercept only model and full model\n\t",
    sum_I_main_full - sum_I_full)
## Heterogenity after fitting the full model
##   0.8616913 
## 
##  Difference heterogeneity between intercept only model and full model
##   0.01210371


Full model results

Web Supplementary Table 2 - Table used to plot Figure 6B. Each estimate corresponds to the predicted effect of each moderator using the predict_rma function from metafor. The column “Coefficients” lists the model intercept and the levels of each moderator. The column “Estimate” is the estimated Pearson (r) correlation. “SE” is the standard error of the estimate. “CI” are the [lower] [upper] bounds of the confidence intervals.

df_pred_tbl <- get_predictions(res_full, format_table = TRUE, clean_labels = TRUE)
df_pred_tbl$Coefficients <- final_mod_names(df_pred_tbl$Coefficients)
pander(df_pred_tbl)
Moderators Coefficients Estimate SE CI
Intercept Intercept 0.344 0.141 [0.081] [0.563]
Index ADI 0.226 0.147 [-0.061] [0.478]
Index AEI 0.067 0.152 [-0.228] [0.351]
Index AR 0.085 0.17 [-0.246] [0.399]
Index BIO 0.211 0.149 [-0.08] [0.469]
Index H 0.506 0.14 [0.273] [0.682]
Index NDSI 0.416 0.148 [0.15] [0.626]
Bio Species abundance 0.271 0.164 [-0.047] [0.539]
Bio Species diversity 0.307 0.144 [0.033] [0.537]
Bio Abundance of sounds 0.55 0.211 [0.197] [0.777]
Environment Aquatic 0.285 0.142 [0.012] [0.519]
Source Acoustic 0.336 0.108 [0.136] [0.51] 
df_pred <- get_predictions(res_full)
colnames(df_pred) <- tolower(str_remove(colnames(df_pred), "\\s.*"))
df_pred$moderators <- df_pred_tbl$Moderators
df_pred$coef <- factor(df_pred_tbl$Coefficients, 
                       levels = rev(df_pred_tbl$Coefficients))

plt_colors <- c("#000000", brewer.pal(n = 4, name = "Dark2"))
ggplot(data = df_pred, aes(x = z2r(estimate), y = coef, 
                           color = moderators)) + 
    geom_point(size = 3) +
    geom_errorbarh(aes(xmin = ci.lb, xmax = ci.ub), height = 0, size = 1) +
    geom_vline(xintercept = 0, linetype = 1) + 
    scale_color_manual(values = plt_colors, name = "Moderators") + 
    theme_minimal() + 
    xlab("Effect size (r)") +
    theme(axis.text.x = element_text(size = 13, color = "black"),
          axis.text.y = element_text(size = 13, color = "black"),
          axis.line.x = element_line(color = "black"),
          axis.line.y = element_line(color = "black"),
          axis.title.y = element_blank(),
          axis.title.x = element_text(size = 14),
          panel.grid.major.y = element_blank(),
          legend.title = element_text(hjust = 0.5, size = 14),
          legend.text = element_text(size = 14)
    )

Figure 6B - Mean estimates (circles) and corresponding 95% confidence intervals (horizontal lines) represented as Pearson correlation (r) effect sizes. Each estimate (except the intercept) corresponds to the additive effect of each coefficient as obtained with the predict_rma function from metafor R package. Estimated effect sizes whose 95% confidence intervals do not overlap zero (black vertical line) indicate a positive correlation between acoustic indices and diversity if they are to the right of zero, or a negative correlation if they are to the left of zero. Moderators are acoustic indices (Index), diversity metrics (Bio), environment (Environment) and acoustic source (Source).


Test of moderators

We checked if our choice of moderators explained some of the variation in our effects sizes by computing a Wald-type test on the null hypothesis that moderator levels’ estimates are jointly equal to zero (Viechtbauer et al., 2015).

Table S9 - Wald-type tests for all moderators (first row), and for each moderator separately (remaining rows). “Q” is the Wald statistic; “df” are the degrees of freedom; and “p” is the probability that moderator estimates came from a chi-square distribution, where all estimates are equal to zero. Thus, a p-value < 0.05 gives support against the null hypothesis that moderator levels estimates are equal to zero (i.e. they do not explain variation in effect sizes).

imp_mods <- matrix(nrow = length(mods) + 1, ncol = 4, 
                   dimnames = list(NULL, c("Moderator", "Q", "df", "p")))
imp_mods <- as.data.frame(imp_mods)
# Add importance of all moderators
imp_mods[1, 2:ncol(imp_mods)] <- res_full[c("QM", "m", "QMp")]
imp_mods$Moderator[1] <- "All moderators"

for(i in 2:nrow(imp_mods)){
  wald_test <- anova(res_full, 
                     btt=grep(mods[i - 1], rownames(res_full$b)))[c("QM", "m","QMp")]
  imp_mods[i, 2:ncol(imp_mods)] <- wald_test
}
imp_mods$Moderator[2:nrow(imp_mods)] <- c("Acoustic indices", 
                                          "Diversity metrics", 
                                          "Environment", "Diversity source")
imp_mods <- mutate_if(imp_mods, is.numeric, round, 3)

pander(imp_mods)
Moderator Q df p
All moderators 27.43 11 0.004
Acoustic indices 22.35 6 0.001
Diversity metrics 3.561 3 0.313
Environment 0.196 1 0.658
Diversity source 0.004 1 0.95

We found that acoustic indices explained most of the variation in our full model. Hence, this suggests that acoustic indices are not equally performing when it comes to biodiversity estimation.


Difference between moderator levels

To assess pairwise comparisons between moderator level estimates, we selected the moderator levels with the highest effect size estimates and compared these with effect size estimates for the other levels of the same moderator. For this, we used again a Wald-type test with one degree of freedom, on the null hypothesis that the difference between the two levels is equal to zero. Note that, if a moderator has only two levels, the comparison is directly retrieved from the model output.

Acoustic indices pairwise comparisons

We compared the best indices, H and NDSI, with the other indices. We did not use ACI for comparisons, as comparisons can be obtained directly from the full model output.

H index

The pairwise comparisons for H index suggest that the H index correlates better with biodiversity than the indices ADI, AEI, AR and BIO.


Table S10 - Wald-type tests for the contrasts between acoustic index H with all other acoustic indices. The column “Compared” expresses the difference between the estimate H and the estimate of each of the other acoustic indices. The column “Estimate” is the estimate obtained from the difference expressed in the previous column; “SE” is the standard error of the difference, and CI.lb, CI.up the confidence interval lower and upper bound, respectively; “QM” is the Wald statistic; “p” is the probability that the difference between estimates is equal to zero. Thus, a p-value < 0.05 gives support against the null hypothesis of no difference between the estimate of the H index and the estimate of the other index.

# Test differences between H and other indices
H_comp <- compare_moderators(df_full, res_full, "index", "H")
kable(H_comp, format = "html", digits = 3) %>%
  kable_styling(c("strip", "condensed"))
Compared Estimate SE CI.lb CI.ub
H - ADI 0.327 0.122 0.089 0.566 7.223 0.007
H - AEI 0.489 0.127 0.241 0.738 14.901 0.000
H - AR 0.471 0.152 0.173 0.769 9.623 0.002
H - BIO 0.343 0.124 0.099 0.586 7.591 0.006
H - ACI 0.198 0.109 -0.016 0.412 3.280 0.070
H - NDSI 0.114 0.130 -0.141 0.369 0.765 0.382
NDSI index

The pairwise comparison for NDSI index suggest that the NDSI index correlates better with biodiversity than the indices AEI and AR.


Table S11 - Wald-type tests for the contrasts between acoustic index NDSI with all other acoustic indices. The column “Compared” expresses the difference between the estimate NDSI and the estimate of each of the other acoustic indices. The column “Estimate” is the estimate obtained from the difference expressed in the previous column; “SE” is the standard error of the difference, and CI.lb, CI.up the confidence interval lower and upper bound, respectively; “QM” is the Wald statistic; “p” is the probability that the difference between estimates is equal to zero. Thus, a p-value < 0.05 gives support against the null hypothesis of no difference between the estimate of the NDSI index and the estimate of the other index.

# Test differences between NDSI and other indices
NDSI_comp <- compare_moderators(df_full, res_full, "index", "NDSI")
kable(NDSI_comp, format = "html", digits = 3) %>%
  kable_styling(c("strip", "condensed"))
Compared Estimate SE CI.lb CI.ub
NDSI - ADI 0.213 0.133 -0.047 0.474 2.586 0.108
NDSI - AEI 0.376 0.138 0.106 0.645 7.442 0.006
NDSI - AR 0.358 0.161 0.041 0.674 4.914 0.027
NDSI - BIO 0.229 0.135 -0.036 0.494 2.869 0.090
NDSI - H -0.114 0.130 -0.369 0.141 0.765 0.382
NDSI - ACI 0.084 0.126 -0.163 0.331 0.444 0.505
Biodiversity metrics pairwise comparisons

Since using abundance of sounds as a diversity metric seemed to be related with a better correlation between acoustic indices and biodiversity, we use abundance of sounds to compute pairwise comparisons with the other diversity metrics.

The pairwise comparison for abundance of sounds gave marginal support (at p < 0.05) for the null hypothesis of no difference between abundance of sounds and other diversity metrics.


Table S12 - Wald-type tests for the contrasts between the diversity metric abundance of sounds with all other diversity metrics. The column “Compared” expresses difference between the estimate abundance of sounds and the estimate of each of the other diversity metrics. The column “Estimate” is the estimate obtained from the difference expressed in the previous column; “SE” is the standard error of the difference, and CI.lb, CI.up the confidence interval lower and upper bound, respectively; “QM” is the Wald statistic; “p” is the probability that the difference between estimates is equal to zero. Thus, a p-value < 0.05 gives support against the null hypothesis of no difference between the estimate of the abundance sounds metric and the estimate of the other metric.

# Test difference between sound_abundance and other bio levels
sound_abund_comp <- compare_moderators(df_full, res_full, "bio", "sound_abundance")
sound_abund_comp$Compared <- c("Abundance of sounds - Species abundance",
                               "Abundance of sounds - Species diversity",
                               "Abundance of sounds - Species richness")
kable(sound_abund_comp, format = "html", digits = 2) %>%
  kable_styling(c("strip", "condensed"))
Compared Estimate SE CI.lb CI.ub
Abundance of sounds - Species abundance 0.34 0.21 -0.08 0.76 2.53 0.11
Abundance of sounds - Species diversity 0.30 0.17 -0.03 0.64 3.09 0.08
Abundance of sounds - Species richness 0.26 0.15 -0.03 0.55 3.13 0.08


Publication bias

We assessed publication bias both, visually (with funnel plots) and statistically (with Egger’s regression).

A funnel plot usually shows the relationship between effect sizes and standard errors. In a symmetric funnel plot, the dispersion of effect sizes should get narrower as standard error decreases.

Due to the multilevel structure of our data set, we used meta-analytic residuals instead of effect sizes to reduce the effect of independence assumptions. We should consider publication bias as an issue if residuals are outside the expected symmetry of the funnel shape, and if some of the funnel sections do not contain any residual.

To statistically test for funnel plot symmetry, we used Egger’s regression with no intercept. A non-significant inverse variance weighted regression of the residuals over the standard error, indicates that the deviation of the residuals from the funnel plot shape is not greater than what would be expected by chance in a symmetric funnel plot.

# Testing model residuals
resid <- rstandard(res_full)
eggers <- regtest(x = resid$resid, sei =sqrt(df_full$var), model = "lm")
funnel(res_full, 
       back = "white", 
       xlab = "Model residuals", 
       ylab = "Effect size standard error", 
       pch = 21,
       col = "darkblue",
       cex = 1.2,
       cex.lab = 1.3,
       cex.axis = 1.1,
       lwd = 2
)
# Put eggers regression results on funnel plot
eggers_round <- round(eggers$pval, 2)
plt_text <- paste0("Regression test for plot symmetry \n\t\t  p = ", eggers_round, "\n\n")
legend(legend = plt_text, x = 0.5, y = -0.01, bg = alpha("darkgrey", 0.2))

Figure 7 - Funnel plot (dashed triangle) with the relation between model residuals from the meta-regression model, and effect size standard error. Absence of publication bias is represented by a scattered and symmetric distribution of values (blue hollow dots) within the funnel. The box on the top right is the p-value from Egger’s regression, which means that we failed to reject the null hypothesis of funnel symmetry (p = 0.53).

Output of Egger’s regression. 
eggers$fit
## 
## Call:
## lm(formula = yi ~ X - 1, weights = 1/vi)
## 
## Coefficients:
##       X     Xsei  
## -0.1235   0.1538

We could not find strong indications of publication bias. Notwithstanding the visual inspection of the funnel plot shows that there are some gaps in the dispersion of the dots in the funnel plot (special at the top, and at the bottom left corner).


Sensibility analysis

Pseudoreplication

Pseudoreplicated designs were widespread in our selected studies (40% of the articles). Therefore, to determine the influence of effect size estimates from pseudoreplicated studies in our meta-analysis, we contrasted the effect size estimate for pseudoreplicated and non-pseudoreplicated studies. For this we conducted a meta-analysis with pseudoreplication as the single binary moderator and observed if the resulting estimate of the contrast between both type of studies overlapped zero.

Meta-analysis with pseudoreplication moderator 
no_out <- capture.output({
            df_pseudorep <-  clear_moderators(df_tidy, "pseudoreplication")  
          })
  
res_pseudorep <- rma.mv(z, var, random = ~1 | id/entry, mods = ~ pseudoreplication, 
                        data = df_pseudorep)
res_pseudorep
## 
## Multivariate Meta-Analysis Model (k = 364; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0508  0.2254     34     no        id 
## sigma^2.2  0.1754  0.4188    364     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 362) = 2176.1806, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4077, p-val = 0.5231
## 
## Model Results:
## 
##                       estimate      se     zval    pval    ci.lb   ci.ub     ​ 
## intrcpt                 0.3759  0.0713   5.2759  <.0001   0.2363  0.5156  *** 
## pseudoreplicationYES   -0.0787  0.1232  -0.6385  0.5231  -0.3201  0.1628      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(data = df_pseudorep, aes(x = pseudoreplication, y = z2r(z))) +
  geom_boxplot(aes(color = pseudoreplication)) + 
  geom_jitter(aes(color = pseudoreplication, size = n), width = 0.2, alpha = 0.5) +
  geom_hline(yintercept = z2r(res_main$b), color = "chartreuse4", linetype = 2) + 
  ylab("Effect Size (r)") + 
  xlab("Pseudoreplicated") + 
  scale_color_brewer(palette = "Set1") + 
  scale_x_discrete(labels = c("No", "Yes")) +
  coord_flip() +
  theme_minimal() +
  theme(legend.position = "none",
        axis.text.y = element_text(size = 12, color = "black"),
        axis.text.x = element_text(size = 11, color = "black"),
        axis.title.y = element_text(size = 14),
        axis.title.x = element_text(size = 12),
        axis.line.x = element_line(color = "black")
  )

Web Supplementary Figure 1 - Boxplot comparing effect size mean values of sampling designs considered pseudoreplicated (“Yes” on the vertical axis) against sampling designs not considered pseudoreplicated (“No” on the vertical axis). The circles represent each individual effect size mean value, and the circle size indicates the relative sample size of the effect size. The dashed green vertical line shows the position of summary effect size obtained from the intercept only meta-analysis.


We failed to find differences between the estimates of pseudoreplicated and non-pseudoreplicated designs. Thus, in general, pseudoreplicated studies provided effect sizes similar to non-pseudoreplicated studies.

Outliers

We visually inspected the presence of outlier studies using Cook’s distance clustered by studies. The Cook’s distance for a given study, refers to how far, on average, effect size estimates will move if the study in question is dropped from model fitting (Viechtbauer & Cheung, 2010). We considered a study an outlier if its Cook’s distance was higher than the average of all computed Cook’s distances.

Cook’s distance

### Cook's distances for each study!

cooks_dist <- cooks.distance(res_full, cluster=df_full$id)
df_full$id <- as.character(df_full$id)

df_cooks <- data.frame(id = names(cooks_dist), cooks_dist = cooks_dist)

df_cooks <- df_cooks %>% 
              left_join(df_full, by = "id") %>%
              select(id, authors, cooks_dist)
# Remove leading spaces
df_cooks$authors <- str_remove(df_cooks$authors, "^\\s")
df_cooks <- df_cooks %>%
              filter(!duplicated(authors)) %>%
              arrange(authors)
df_cooks$authors <- author_format(df_cooks$authors)

ggplot(data = df_cooks, aes(x = authors, y = cooks_dist, group = 1)) + 
  geom_point(color = "deepskyblue4") +
  geom_line(color = "deepskyblue4") + 
  geom_segment(aes(xend=authors), yend = 0, color = "darkgrey", linetype = 2) + 
  geom_hline(yintercept = mean(cooks_dist), color = "darkred", linetype = 2) + 
  xlab("Studies") + ylab("Cook\u2019s distance") + 
  scale_x_discrete(limits = rev(df_cooks$authors)) + 
  theme_minimal() + 
  theme(
        axis.line = element_line(color = "black"),
        axis.text.y = element_markdown(color = "black", size = 12),
        axis.title.y = element_markdown(color = "black", size = 16),
        axis.text.x = element_text(color = "black", size = 12),
        axis.title.x = element_markdown(color = "black", size = 16),
  ) + 
  coord_flip()

Figure S5 - Cook’s distance values for each study (blue dots) and average Cook’s distance over all studies (dashed vertical red line). The Cook’s distance for a given study can be interpreted as the distance between the entire set of predicted values once with this study included and once with this study excluded from the model fitting procedure. On the y-axis are the studies identified by first author and year. The x-axis corresponds to the Cook’s distance values.

Check outlier studies

Here, we examine outlier studies to discriminate possible reasons for their influence.

outliers <- df_cooks[which(df_cooks$cooks_dist > mean(df_cooks$cooks_dist)), ]$id

df_outliers <- df_full[which(df_full$id %in% outliers), ]

ggplot(data = df_outliers, aes(x = z2r(z), y = id)) + 
  geom_boxplot(aes(color = id), fill = NA, width = 0.3) + 
  geom_jitter(height = 0.1, aes(color = id)) + 
  scale_color_brewer(palette = "Set2") + 
  scale_y_discrete(labels = rev(unique(df_outliers$authors))) + 
  xlab("Effect size (r)") +
  geom_vline(xintercept = z2r(res_main$b), color = "chartreuse4", linetype = 2) + 
  theme_minimal() + 
  theme(
    axis.title.y = element_blank(),
    axis.text.y = element_text(size = 11, color = "black"),
    axis.text.x = element_text(size = 11, color = "black"),
    axis.title.x = element_text(size = 13),
    axis.line.x = element_line(color = "black"),
    legend.position = "none"
  )

Figure S6 - Boxplot and distribution of effect size values (dots) of the two studies identified as outliers. The y-axis identifies the study, and the x-axis corresponds to the Pearson r effect size. The green vertical dashed line is the summary effect obtained in the intercept-only model.


Both outlier studies used birds as their studied organism, and assessed multiple acoustic indices (Gage et al. (2017) examined ACI, ADI, AEI, BIO, H, NDSI indices; and Mammides et al. (2017) examined ACI, ADI, AEI, AR, BIO, H, NDSI indices). The main difference was that Gage et al. (2017) used acoustic recordings to get biodiversity measures while Mammides et al. (2017) relied on non-acoustic sources of biodiversity information.

The box plots and effect size dispersion, suggest that the study by Gage et al. (2017) contributed overdispersed effect sizes values, including some at the lower and higher ends of the Pearson effect size scale [-1, 1].

The Mammides et al. (2017) study contributed a total 84 effect sizes, also dispersed over a wide range. The number of effect sizes per se (23% of total effect sizes) could be responsible for its high value of Cook’s distance.

Removing outlier studies and examining results

We evaluated the robustness of our results by removing the outliers from the data set and running the meta-regression model without the outlier studies.

Meta-analysis with outliers removed 
df_no_outliers <- df_full[-which(df_full$id %in% outliers), ]

res_no_outliers <- rma.mv(z, var, random = ~1 | id/entry, 
                   mods = ~ index + bio + environ + diversity_source, 
                   data = df_no_outliers)

res_no_outliers
## 
## Multivariate Meta-Analysis Model (k = 200; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.1225  0.3500     31     no        id 
## sigma^2.2  0.0557  0.2360    200     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 188) = 472.4368, p-val < .0001
## 
## Test of Moderators (coefficients 2:12):
## QM(df = 11) = 6.3074, p-val = 0.8521
## 
## Model Results:
## 
##                           estimate      se     zval    pval    ci.lb   ci.ub​ 
## intrcpt                     0.6457  0.1870   3.4522  0.0006   0.2791  1.0123 
## indexADI                   -0.2059  0.1118  -1.8412  0.0656  -0.4251  0.0133 
## indexAEI                   -0.0753  0.1260  -0.5977  0.5500  -0.3224  0.1717 
## indexAR                    -0.0770  0.2082  -0.3700  0.7114  -0.4850  0.3310 
## indexBIO                   -0.0992  0.1192  -0.8319  0.4054  -0.3328  0.1345 
## indexH                     -0.0357  0.1058  -0.3372  0.7360  -0.2430  0.1716 
## indexNDSI                  -0.0164  0.1406  -0.1165  0.9073  -0.2919  0.2591 
## bioabundance               -0.1566  0.1512  -1.0355  0.3005  -0.4530  0.1398 
## biodiversity               -0.1064  0.1298  -0.8199  0.4122  -0.3607  0.1479 
## biosound_abundance          0.1597  0.2105   0.7587  0.4480  -0.2528  0.5722 
## environA                   -0.1907  0.2057  -0.9271  0.3539  -0.5939  0.2125 
## diversity_sourceacoustic   -0.1379  0.1909  -0.7226  0.4700  -0.5120  0.2362 
##  
## intrcpt                   *** 
## indexADI                    . 
## indexAEI 
## indexAR 
## indexBIO 
## indexH 
## indexNDSI 
## bioabundance 
## biodiversity 
## biosound_abundance 
## environA 
## diversity_sourceacoustic 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# With outliers results from full model results 
df_pred$df <- "with_outliers"
# No outliers resuls in dataframe
df_no_outliers_plt <- get_predictions(res_no_outliers)
df_no_outliers_plt$moderators <- df_pred$moderators
df_no_outliers_plt$coef <- factor(df_pred$coef, 
                                  levels = rev(df_pred$coef))
df_no_outliers_plt$df <- "no_outliers"

df_examine_outliers <- rbind(df_pred, df_no_outliers_plt)

plt_colors <- c("skyblue4", "goldenrod3")#"seagreen")
pd <- position_dodge(0.6)
n_rows <- nrow(res_full$b)

ggplot(data = df_examine_outliers, aes(x = estimate, y = coef, color = df, position = df)) + 
    geom_point(position = pd, size = 2.3) +
    geom_errorbarh(aes(xmin = ci.lb, xmax = ci.ub), height = 0, position = pd, size = 0.8) + 
    geom_vline(xintercept = 0, linetype = 1, color = "black") + 
    #scale_y_discrete(labels =  rev(y_labels)) +
    scale_color_manual(values = plt_colors, name = "Data set", 
                       labels = c("No outliers", "Full data set")) + 
    geom_hline(yintercept= seq(1, n_rows - 1) + 0.5, linetype = 3, color = "black") + 
    theme_minimal() + 
    xlab("Effect size (r)") +
    theme(axis.text.x = element_text(size = 12, color = "black"),
          axis.text.y = element_text(size = 13, color = "black"),
          axis.line.x = element_line(color = "black"),
          axis.line.y = element_line(color = "black"),
          axis.title.y = element_blank(),
          axis.title.x = element_text(size = 14),
          panel.grid.major.y = element_blank(),
          legend.title = element_text(hjust = 0.5, size = 12),
          legend.text = element_text(size = 11)
    )

Figure S7 - Contrast of model estimates obtained with meta-regression analysis over the full data set (yellow) and over the data set with outliers removed (blue). Estimates are represented with circles and corresponding 95% confidence intervals with horizontal lines. Each estimate corresponds to the additive effect when a moderator level is replaced in the intercept (e.g. ADI is the additive effect of ADI when ADI is put as intercept instead of ACI). Estimated effect sizes whose 95% confidence intervals do not overlap zero (black vertical line) indicate a positive correlation between acoustic indices and diversity if they are to the right of zero, or a negative correlation if they are to the left of zero. We considered outliers every study that had a Cook’s distance value higher than the mean of all Cook distances. Model moderators were acoustic indices (ADI, AEI, AR, BIO, H, NDSI, with ACI as intercept), diversity metric (Species abundance, Species diversity, Abundance of sounds, with Species richness as intercept), environment (Aquatic, with Terrestrial as intercept), diversity source (Acoustic, with Non-Acoustic as intercept). The solid vertical black line represents a null effect size.


The results are similar with major overlap between confidence intervals, especially for the most conclusive results in the full data set. It seems that removing outliers tended to generate stronger mean effect size estimates of the correlation between acoustic indices and biodiversity.

Heterogeneity for the intercept-only model with no outliers 
res_main_no_outliers <- rma.mv(z, var, random = ~1 | id/entry, data = df_no_outliers)

Is_no_outliers <- multilevel_I(res_main_no_outliers)

sum(Is_no_outliers)
## [1] 0.8256989

Effect size tendencies

We visually inspected tendencies in the effect size over the year of publication, and impact factor of the journal.

Publication year

We observed a tendency of effect size values to decrease over the years, which is distinctive from 2007 to 2015 and 2015-2019, where the latter period saw a surge in the number of published studies.

lin <- rma.mv(z, var, random = ~ 1 | id/entry, mods = ~ year, data = df_tidy)
lin
## 
## Multivariate Meta-Analysis Model (k = 364; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0159  0.1262     34     no        id 
## sigma^2.2  0.1707  0.4132    364     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 362) = 2191.0423, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 22.7659, p-val < .0001
## 
## Model Results:
## 
##          estimate       se     zval    pval     ci.lb     ci.ub     ​ 
## intrcpt  220.9027  46.2337   4.7780  <.0001  130.2862  311.5192  *** 
## year      -0.1094   0.0229  -4.7714  <.0001   -0.1543   -0.0644  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#print(z2r(coefficients(lin)))
#print(z2r(lin$ci.lb))
#print(z2r(lin$ci.ub))
plt_color <- "darkorchid4"

preds <- predict(lin, interval = "confidence")
df_tidy$y <- z2r(preds$pred)
df_tidy$ymin <- z2r(preds$ci.lb)
df_tidy$ymax<- z2r(preds$ci.ub)

xs <- range(df_tidy$year)
ys <- z2r((cbind(1, xs) %*% lin$b))

ggplot(df_tidy, aes(x = year, y = z2r(z))) + 
    #geom_segment(aes(x = xs[1], xend = xs[2], y = ys[1], yend = ys[2]), color = plt_color, size = 0.3, linetype = "dashed") + 
    geom_jitter(aes(size = n), shape = 21, 
                fill = alpha(plt_color, 0.5), 
                color = plt_color) + 
    geom_hline(yintercept=0, linetype = 2) +
    geom_line(aes(y = y, x = year), color = plt_color) + 
    geom_ribbon(aes(ymin = ymin, ymax = ymax), alpha = 0.3) +
    labs(y = "Effect size (r)", x = "Publication Year") +  
    scale_x_continuous(breaks = seq(min(df_tidy$year), 
                                    max(df_tidy$year), by = 2)) +
    theme_minimal() +
    theme(
        axis.line.y = element_line(color = "black"),
        axis.text = element_text(color = "black", size = 12),
        axis.title = element_text(color = "black", size = 14),
        legend.position = "none"
    )

Figure 8A - Relationship between effect size mean values (circles) and publication year after 2015 (inclusive). Circle size indicates the relative sample size for each effect size. The fitted line is a meta-regression over the year of publication with the corresponding 95% confidence interval region shaded grey. The dashed horizontal line represents an effect size of 0. Effect size mean values are positioned along the impact factor axis with minor random noise to reduce overlapping. Model statistics in Pearson correlation r estimate [CI], intercept 1.000 [1.000, 1.000], slope -0.109 [-0.153, -0.064]. The computed model is a linear model using Fisher’s Z as effect size. The transformation from Fisher’s Z unbounded values to Pearson correlation values bounded between -1 and 1 creates the curved pattern for larger effect sizes.

Publication year > 2015

The tendency continues less pronounced from 2015 to 2019. As our knowledge on acoustic indices progresses, larger limitations on the capacity of these tools to efficiently quantify local biodiversity are revealed. Thus, together with the rapid spread of these indices, additional efforts are required to better understand the significance, interpretation, and efficient use of these indices in biodiversity appraisal (Gasc et al., 2015)

df_tidy_after2015 <- df_tidy[df_tidy$year >= 2015, ]
lin <- rma.mv(z, var, random = ~ 1 | id/entry, mods = ~ year, data = df_tidy_after2015)
lin
## 
## Multivariate Meta-Analysis Model (k = 351; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0175  0.1323     25     no        id 
## sigma^2.2  0.1720  0.4148    351     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 349) = 2099.2807, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.8158, p-val = 0.0508
## 
## Model Results:
## 
##          estimate       se     zval    pval    ci.lb     ci.ub   ​ 
## intrcpt  164.8134  84.2411   1.9564  0.0504  -0.2962  329.9230  . 
## year      -0.0816   0.0418  -1.9534  0.0508  -0.1634    0.0003  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#print(z2r(coefficients(lin)))
#print(z2r(lin$ci.lb))
#print(z2r(lin$ci.ub))
preds <- predict(lin, interval = "confidence")
df_tidy_after2015$y <- z2r(preds$pred)
df_tidy_after2015$ymin <- z2r(preds$ci.lb)
df_tidy_after2015$ymax<- z2r(preds$ci.ub)

ggplot(df_tidy_after2015, aes(x = year, y = z2r(z))) + 
    geom_jitter(aes(size = n), shape = 21, 
                fill = alpha(plt_color, 0.5), 
                color = plt_color) + 
    geom_hline(yintercept=0, linetype = 2) +
    geom_line(aes(y = y, x = year), color = plt_color) + 
    geom_ribbon(aes(ymin = ymin, ymax = ymax), alpha = 0.3) +
    labs(y = expression(paste("Effect size (", italic("r"), ")")), x = "Publication Year") +  
    scale_x_continuous(limits = c(2015, 2019),
                       breaks = seq(min(df_tidy_after2015$year), 
                                    max(df_tidy_after2015$year), 
                                    by = 1)) +
    theme_minimal() +
    theme(
        axis.line.y = element_line(color = "black"),
        axis.text = element_text(color = "black", size = 12),
        axis.title = element_text(color = "black", size = 14),
        legend.position = "none"
    )

Figure 8B - Relationship between effect size mean values (circles) and publication year after 2015 (inclusive). Circle size indicates the relative sample size for each effect size. The fitted line is a meta-regression over the year of publication with the corresponding 95% confidence interval region shaded grey. The dashed horizontal line represents an effect size of 0. Effect size mean values are positioned along the impact factor axis with minor random noise to reduce overlapping. Model statistics in Pearson correlation r estimate [CI], intercept 1.000 [-0.289, 1.000], slope -0.081 [-0.162, 0.000].

Impact factor

Effect size values do not appear to exhibit a tendency when it comes to journal impact factor.

df_tidy_if <- filter(df_tidy, !is.na(impact_factor))
lin <- rma.mv(z, var, random = ~ 1 | id/entry, mods = ~ impact_factor, data = df_tidy_if)
lin
## 
## Multivariate Meta-Analysis Model (k = 316; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0475  0.2180     32     no        id 
## sigma^2.2  0.1713  0.4139    316     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 314) = 2062.4310, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0745, p-val = 0.7850
## 
## Model Results:
## 
##                estimate      se     zval    pval    ci.lb   ci.ub   ​ 
## intrcpt          0.4249  0.1652   2.5714  0.0101   0.1010  0.7487  * 
## impact_factor   -0.0125  0.0457  -0.2729  0.7850  -0.1021  0.0772    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#print(z2r(coefficients(lin)))
#print(z2r(lin$ci.lb))
#print(z2r(lin$ci.ub))
preds <- predict(lin, interval = "confidence")
df_tidy_if$y <- z2r(preds$pred)
df_tidy_if$ymin <- z2r(preds$ci.lb)
df_tidy_if$ymax<- z2r(preds$ci.ub)

plt_color <- "deeppink4"
ggplot(df_tidy_if, aes(x = impact_factor, y = z2r(z))) + 
  geom_jitter(aes(size = n), shape = 21, 
              fill = alpha(plt_color, 0.5), 
              color = plt_color,
              width = 0.2) + 
    geom_hline(yintercept=0, linetype = 2) +
    geom_line(aes(y = y, x = impact_factor), color = plt_color) + 
    geom_ribbon(aes(ymin = ymin, ymax = ymax), alpha = 0.3) +
    labs(y = "Effect size (r)", x = "Impact Factor") +  
    theme_minimal() +
    theme(
        axis.line.y = element_line(color = "black"),
        axis.text = element_text(color = "black", size = 12),
        axis.title = element_text(color = "black", size = 14),
        legend.position = "none"
    )

Figure S4 - Relationship between effect size mean values (circles) and journal impact factor. Circle size indicates the relative sample size for each effect size. The fitted line is a meta-regression over the journal impact factor with the corresponding 95% confidence interval region shaded grey. The dashed horizontal line represents an effect size of 0. Effect size mean values are positioned along the impact factor axis with minor random noise to reduce overlapping. Model statistics in Pearson correlation r estimate [CI], intercept 0.401 [0.100, 0.164], slope -0.012 [-0.102, 0.077].

Supplementary data

All code files and supplementary data used in the study can be found in https://github.com/irene-alcocer/Acoustic-Indices.

Session info

sessionInfo()
## R version 4.2.0 (2022-04-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Arch Linux
## 
## Matrix products: default
## BLAS:   /usr/lib/libopenblasp-r0.3.20.so
## LAPACK: /usr/lib/liblapack.so.3.10.1
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] RColorBrewer_1.1-3    ggtext_0.1.1          ggplot2_3.3.6        
##  [4] pander_0.6.5          kableExtra_1.3.4.9000 compute.es_0.2-5     
##  [7] metafor_3.4-0         metadat_1.2-0         Matrix_1.4-1         
## [10] stringr_1.4.0         dplyr_1.0.9           png_0.1-7            
## [13] knitr_1.39           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.1.2    xfun_0.31           pbapply_1.5-0      
##  [4] purrr_0.3.4         lattice_0.20-45     colorspace_2.0-3   
##  [7] vctrs_0.4.1         generics_0.1.2      htmltools_0.5.2    
## [10] viridisLite_0.4.0   yaml_2.2.1          utf8_1.2.2         
## [13] rlang_1.0.2         gridtext_0.1.4.9000 pillar_1.7.0       
## [16] glue_1.6.2          withr_2.5.0         DBI_1.1.2          
## [19] lifecycle_1.0.1     munsell_0.5.0       gtable_0.3.0       
## [22] rvest_1.0.2         evaluate_0.15       labeling_0.4.2     
## [25] fastmap_1.1.0       parallel_4.2.0      markdown_1.1       
## [28] fansi_0.4.1         highr_0.8           Rcpp_1.0.5         
## [31] scales_1.2.0        webshot_0.5.3       farver_2.1.0       
## [34] systemfonts_1.0.4   digest_0.6.27       stringi_1.7.6      
## [37] grid_4.2.0          mathjaxr_1.6-0      cli_3.3.0          
## [40] tools_4.2.0         magrittr_2.0.1      tibble_3.1.7       
## [43] crayon_1.5.1        pkgconfig_2.0.3     ellipsis_0.3.2     
## [46] xml2_1.3.3          assertthat_0.2.1    rmarkdown_2.6      
## [49] svglite_2.1.0       httr_1.4.2          rstudioapi_0.13    
## [52] R6_2.5.0            nlme_3.1-157        compiler_4.2.0

References

Buxton, R.T., McKenna, M.F., Clapp, M., Meyer, E., Stabenau, E., Angeloni, L.M., Crooks, K. & Wittemyer, G. (2018) Efficacy of extracting indices from large-scale acoustic recordings to monitor biodiversity. Conservation Biology 32, 1174–1184. Wiley Online Library.
Del Re, A. & Del Re, M.A. (2012) Package ‘compute. Es’. https://cran.r-project.org/web/packages/compute.es/index.html.
Gage, S.H., Wimmer, J., Tarrant, T. & Grace, P.R. (2017) Acoustic patterns at the samford ecological research facility in south east queensland, australia: The peri-urban SuperSite of the terrestrial ecosystem research network. Ecological Informatics 38, 62–75. Elsevier.
Koricheva, J., Gurevitch, J. & Mengersen, K. (2013) Handbook of meta-analysis in ecology and evolution. Princeton University Press.
Mammides, C., Goodale, E., Dayananda, S.K., Kang, L. & Chen, J. (2017) Do acoustic indices correlate with bird diversity? Insights from two biodiverse regions in yunnan province, south china. Ecological Indicators 82, 470–477. Elsevier.
Nakagawa, S. & Cuthill, I.C. (2007) Effect size, confidence interval and statistical significance: A practical guide for biologists. Biological reviews 82, 591–605. Wiley Online Library.
Rohatgi, A. (2019) WebPlotDigitizer, version 4.2. https://apps.automeris.io/wpd/.
Sueur, J., Farina, A., Gasc, A., Pieretti, N. & Pavoine, S. (2014) Acoustic indices for biodiversity assessment and landscape investigation. Acta Acustica united with Acustica 100, 772–781. S. Hirzel Verlag.
Sugai, L.S.M., Silva, T.S.F., Ribeiro Jr, J.W. & Llusia, D. (2019) Terrestrial passive acoustic monitoring: Review and perspectives. BioScience 69, 15–25. Oxford University Press.
Viechtbauer, W. & Cheung, M.W.-L. (2010) Outlier and influence diagnostics for meta-analysis. Research synthesis methods 1, 112–125. Wiley Online Library.
Viechtbauer, W., López-López, J.A., Sánchez-Meca, J. & Marı́n-Martı́nez, F. (2015) A comparison of procedures to test for moderators in mixed-effects meta-regression models. Psychological methods 20, 360. American Psychological Association.
Zuur, A.F., Ieno, E.N. & Elphick, C.S. (2010) A protocol for data exploration to avoid common statistical problems. Methods in ecology and evolution 1, 3–14. Wiley Online Library.