This project investigates the potential of iNaturalist as a powerful alternative or supplementary method for estimating species richness—the total number of different species—in a given area, moving beyond costly and time-consuming traditional field surveys. We chose New Zealand as our case study due to its well-documented, yet complex, biodiversity (estimated at ~52,000), allowing us to directly compare the performance of this highly popular crowdsourced species identification platform against official, expert-derived records. The overarching goal is to enhance global biodiversity conservation strategies amidst the ongoing sixth mass extinction event.

Our methodology involved downloading iNaturalist’s observation data from the Global Biodiversity Information Facility (GBIF) and comparing it against New Zealand’s official species data (NZTCS). Due to incompatibilities in the data, we used ACE and Chao1 asymptotic estimators to extrapolate species richness from the iNaturalist observations. Our primary finding revealed a significant disparity between the estimates: iNaturalist projected approximately 20,000 species, falling considerably short of the official 50,000 to 55,000 species estimate. This large discrepancy is primarily attributed to a major shortcoming of citizen science: uneven sampling effort. iNaturalist data showed a strong sampling bias towards highly populated areas and established trails, meaning vast regions of the country are underrepresented. Despite this, iNaturalist showed promise in documenting species richness more effectively than traditional methods for certain datasets, suggesting its strong potential as a valuable supplementary tool. Future work should focus on developing new abundance estimators specifically tailored for the geographically biased and expansive datasets characteristic of citizen science.

The project faced limitations due to the nature of the available data, including a lack of geolocation in the NZTCS dataset and the inherent identification uncertainty in iNaturalist (observations labeled “Research Grade” are about 85% accurate).

Looking ahead, we emphasize the need to integrate indigenous knowledge into biodiversity monitoring alongside modern citizen science, particularly in New Zealand, to create a more holistic approach to conservation. We also commit to open science and acknowledge that scientific practice is often shaped by existing power structures, which is why we focused on a diverse methodology and literature to reflect a commitment to inclusive perspectives.