Data from: Drivers and spatial patterns of avian defaunation in tropical forests
Creators
- 1. Estación Biológica de Doñana
- 2. Sapienza University of Rome
- 3. University of Wisconsin–Madison
- 4. Ecologie des Forêts de Guyane
- 5. Muséum national d'Histoire naturelle
- 6. Utrecht University
- 7. Instituto Jurua*
- 8. University of East Anglia
- 9. Museo Nacional de Ciencias Naturales
Description
Notes
Methods
We expanded the dataset of hunting impacts on bird populations from Benítez-López et al. (2017) by supplementing additional bird abundance data from local hunting studies through a systematic search of the literature (see details in Supplementary Methods 1). Our final dataset comprises 2968 abundance estimates for 518 tropical bird species at both hunted and non-hunted sites (control) based on 60 local hunting studies (Figure S1, Table SX). Studies that report potential confounding effects, such as habitat loss and logging were not included in our analysis. Changes in abundance due to hunting pressure were subsequently expressed as the response ratio (RR) between the abundance of each bird species (s) in hunted (Xsh) and non-hunted (Xsc) sites within each study (RR =Xsh/Xsc) (Peres & Palacios, 2007; Benítez-López et al., 2017, 2019) RR = 0 then indicates local extinction; 0 < RR < 1, reduction in abundance; RR ≈ 1, no changes in abundance and RR > 1, increase in abundance.
We compiled the following information from each study: the geographic coordinates of hunted and unhunted sites in each study, the hunter's access point to the hunted site (i.e. roads, settlements or rivers), and the motivation for hunting (i.e. subsistence, commercial or both). We further compiled information on different predictors often recognised as drivers of hunting pressure, including the distance to access points, human population density, poverty level, and travel time to major cities, as well as information on factors that modulate species responses to hunting pressure, such as net primary productivity or whether hunting activities took place inside or outside protected areas (Peres, 2000; Brashares et al., 2010; Benítez-López et al., 2017, 2019; Whytock et al., 2018; Bogoni et al., 2020; Scabin & Peres, 2021) (Supplementary Table 2).
Please, note that all continuous predictors were scaled and centered around zero with a SD equal to 1 before model fitting.
Files
Files
(138.8 kB)
Name | Size | Download all |
---|---|---|
md5:5400f098363e65e8c114247c79a276b5
|
4.1 kB | Download |
md5:3d163efb0a05de04f4d42a433ff94ff2
|
22.7 kB | Download |
md5:72fc50ecec54643b6a106899d63e8c42
|
37.7 kB | Download |
md5:df8d8b563776be926ee056704096208b
|
3.5 kB | Download |
md5:049c090f832f413d473a6b763eaccf5f
|
1.6 kB | Download |
md5:d1c62e8b40c40599bbf6c87105927948
|
1.7 kB | Download |
md5:c55cddc11c0ff532675d1555d9f6f5bf
|
5.4 kB | Download |
md5:7c2433a1388ac75d22acd67daa470140
|
8.5 kB | Download |
md5:008cb4898cf7a765afb515270603ae05
|
3.7 kB | Download |
md5:3ecc66bf5518f63ffef084d443e1f465
|
10.8 kB | Download |
md5:5eb9b79706adc2a1bfb00699e899eb48
|
11.3 kB | Download |
md5:74f0acfc578d3bee551f39b9ae96980c
|
8.7 kB | Download |
md5:e42c5f2038593844a486c54531701fa4
|
12.1 kB | Download |
md5:777cd57f79947d9e34684377af004bd3
|
3.4 kB | Download |
md5:34e82b64f23d4d27cb78d277316bbecc
|
3.6 kB | Download |
Additional details
Related works
- Is source of
- 10.5061/dryad.2z34tmpsw (DOI)