Using long-term citizen science data to understand distribution and habitat use of an irruptive species

Not all who wander are lost: Trail bias in community science

The exponential growth and interest in community science programs is producing staggering amounts of biodiversity data across broad temporal and spatial scales. Large community science datasets such as iNaturalist and eBird are allowing ecologists and conservation biologists to answer novel questions that were not possible before. However, the opportunistic nature of many of these enormous datasets leads to biases. Spatial bias is a common problem, where observations are biased towards points of access like roads and trails. iNaturalist-a popular biodiversity community science platform-exhibits strong spatial biases, but it is unclear how these biases affect the quality of biodiversity data collected. Thus, we tested whether fine-scale spatial bias due to sampling from trails affects taxonomic richness estimates. We compared timed transects with experienced iNaturalist observers on and off trails in British Columbia, Canada. Using generalized linear mixed models, we found higher overall taxonomic richness on trails than off trails. In addition, we found more exotic as well as native taxa on trails than off trails. There was no difference between on and off trail observations for species that are rarely observed. Thus, fine-scale spatial bias from trails does not reduce the quality of biodiversity measurements, a promising result for those interested in using iNaturalist data for research and conservation management.

50 Years of Cumulative Open-Source Data Confirm Stable and Robust Biodiversity Distribution Patterns for Macrofungi

Fungi are a hyper-diverse kingdom that contributes significantly to the regulation of the global carbon and nutrient cycle. However, our understanding of the distribution of fungal diversity is often hindered by a lack of data, especially on a large spatial scale. Open biodiversity data may provide a solution, but concerns about the potential spatial and temporal bias in species occurrence data arising from different observers and sampling protocols challenge their utility. The theory of species accumulation curves predicts that the cumulative number of species reaches an asymptote when the sampling effort is sufficiently large. Thus, we hypothesize that open biodiversity data could be used to reveal large-scale macrofungal diversity patterns if these datasets are accumulated long enough. Here, we tested our hypothesis with 50 years of macrofungal occurrence records in Norway and Sweden that were downloaded from the Global Biodiversity Information Facility (GBIF). We first grouped the data into five temporal subsamples with different cumulative sampling efforts (i.e., accumulation of data for 10, 20, 30, 40 and 50 years). We then predicted the macrofungal diversity and distribution at each subsample using the maximum entropy (MaxEnt) species distribution model. The results revealed that the cumulative number of macrofungal species stabilized into distinct distribution patterns with localized hotspots of predicted macrofungal diversity with sampling efforts greater than approximately 30 years. Our research demonstrates the utility and importance of the long-term accumulated open biodiversity data in studying macrofungal diversity and distribution at the national level.