Scavengers in the human-dominated landscape dataset
Data files
Nov 23, 2024 version files 71.19 KB
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README.md
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scavenging_data.csv
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Abstract
Rapid urbanization is a major cause of habitat and biodiversity loss and human-animal conflict. While urbanization is inevitable, we need to develop a good understanding of the urban ecosystem and the urban-adapted species, in order to ensure sustainable cities for our future. Scavengers play a major role in urban ecosystems, and often, urban adaptation involves a shift towards scavenging behaviour in wild animals. We carried out an experimented at different sites in the state of West Bengal, India, to identify the scavenging guild within urban habitats, in response to human-provided food. Our study found a total of 17 different vertebrate species across 15 sites, over 498 sessions of observations. We carried out network analysis to understand the dynamics of the system, and found that the free-ranging dog and common myna were key species within the scavenging networks. This study revealed the complexity of scavenging networks within human-dominated habitats.
Title: Scavengers in the human-dominated landscape dataset
| Variable | Definition |
|---|---|
| Day_order | The day of the experiment from day 1 |
| Date | Date on which the experiment was performed (DD/MM/YY) |
| Session | Morning/afternoon |
| Habitat | Urban/ rural/ sub-urban |
| Habitat_food | The combination of habitat of the experiment and the type of food provided |
| Place | Identity of the site |
| Food | veg or non-veg |
| Scv | scavenging species |
| Scv_code | code given to the species |
| Latency | time taken for the species to respond, from the start of the experiment (seconds) |
| Order | The order of arrival of the species to the site |
| Sniff | sniffing of the food if observed (yes/no) |
| Eat | Eating of the food (yes/no) |
| Column N to column AE | counts of individuals of a species observed |
| Temp | Local temperature during the time of the experiment (deg Celsius) |
| Wind | wind speed (km/h) |
| Rain | in mm |
| Cloud | cloud cover in percentage, converted to proportion |
| Ses_div | session diversity |
| Ses_divn | normalised session diversity |
The data was collected through video recordings and observations.
