Supporting data from: When birding hotspots get too hot: A geographic evaluation of wildfire-related disturbance on spatiotemporal biases in citizen science data

Long-term monitoring is critical for ecology and conservation, especially as non-stationary climatic conditions increase. Citizen science projects offer long-term georeferenced data from thousands of observers across diverse geographic areas. Despite the attraction of these datasets for biogeographical research and conservation planning, data collection commonly lacks standardized probabilistic sampling, which can increase observer bias, decrease precision of parameter estimates, and increase risk of spurious results when using the associated species data. Additionally, environmental disturbance may affect observer behavior, confounding the observed patterns in species responses. We aimed to test the effects of wildfire disturbance on observer biases in locality selection and return rates by citizen scientists registered with eBird, a globally available bird observation database. Location. Western USA. We used a long-term (10-yr) dataset of 47,662 localities from 1,788 eBird observers t..., Study area
This study focuses on Western United States (USA), which has experienced increased wildfire frequency, size, and severity in recent decades (Figure 2, Weber and Yadav 2020), with these increases predicted to continue due to climate change (Abatzoglou et al. 2021, Wasserman and Mueller 2023, McGinnis et al. 2023). This region supports the widest elevation range in the continental USA (86m BSL to 4,418m ASL), containing large tracks of public land and a high diversity of biomes, including forested mountains, coastal environments, arid and semi-arid plateaus and plains, high-elevation subalpine and alpine areas, and rainforests. In addition to supporting high avian species diversity (> 600 bird species), thousands of bird observers report bird sightings across this region each year.
Citizen-based wildlife observation data
We used data freely available from the eBird repository of citizen-based bird observations as a case study (Sullivan et al. 2009). This data source currentl..., , # Supporting data from: When birding hotspots get too hot: A geographic evaluation of fire-related landscape disturbance on spatiotemporal biases in citizen science data

[https://doi.org/10.5061/dryad.0vt4b8h5x](https://doi.org/10.5061/dryad.0vt4b8h5x)

## Description of the data and file structure

Observer data obtained from the eBird database and fire metrics from the MTBS program to identify and quantify disturbance-related sources of bias in citizen science data. 'NA' values in CSV files will be removed when appropriate using the attached R code.

### Files and variables

#### File: 3_Long_Term_Probability_of_Return_Data.csv

**Description:** 

##### Variables

* join_row: internal analysis
* tsfire: time since fire (years)
* Join_Count: internal analysis
* TARGET_FID: internal analysis
* LOCALIT: observation locality ID
* OBSERVA: observation date
* OBSERVE: observer ID
* YEAR: Year of observation
* DAY_OF_: julian calendar day
* Evnt_ID: observation eBird ID
* BrnBndA: burned ac...,