Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 47 min ago
PreprintCC BY 4.0EcoEvoRxiv

How Much of the Forest Do Citizen Scientists Actually Record? A Decade of iNaturalist Sampling in the Hyrcanian Forests

Abstract

Citizen-science records increasingly inform biodiversity research and conservation planning, yet how much of a large forest they actually cover is rarely measured. We analyzed 1,700 research-grade iNaturalist records from the Hyrcanian Forest, northern Iran (2015–2025), on a 10 × 10 km grid, comparing observed coverage with null models. Annual records rose from 48 to 386, but occupied only 43.7% of forest cells, 41% fewer than an even spread would give, and 56% of cells had no record. Fifteen observers (top 10%) provided 69% of records. In a presence–background model with clus-ter-robust errors, only elevation was independently associated with record presence (odds ratio 0.92 per 100 m); distances to roads and settlements were not. Because steep terrain limits where people can walk and record, similar eleva-tion-related gaps may occur in other mountain forests, where record counts could overstate coverage. Models using such records should adjust for elevation bias.

Related

  1. Guardians of our Rivers: transforming river health assessment through macroinvertebrate monitoring with citizen scientists
    R. Lewis, K. Dodd, C. Macadam +2
    bioRxivPreprintCC BY-NC-NDFull textEcologybioRxiv
  2. iNaturalist records reveal shifts in phenology of caterpillar (Lepidoptera) activity in response to warmer spring temperatures
    Journal of insect science (Online)Peer-reviewedCC BY-NCFull textGeneral biologyEurope PMC
  3. Smart insect trap: insect identification and classification using iNaturalist and ChatGPT
    Yuhan Wu, Anthony Nicolaides, Anthony Coffin-Schmitt +1
    Royal Society Open SciencePeer-reviewedCC BY 4.0Full textPlant biologyCrossrefEurope PMCPMC
  4. Seasonal variation in the density of the Persian leopard in the Hyrcanian forests of northern Iran
    Mehdi Tizrouyan, Ali Lotfi, Farid Salmanpour +8
    Global Ecology and ConservationPeer-reviewedCC BYFull textEcologyDOAJ
  5. Interpretable metaheuristic-optimized machine learning for predicting biodiversity indices in the Hyrcanian forest
    Hamed Asadi, Abolfazl Jaafari, Mahya Tafazoli +1
    Ecological IndicatorsPeer-reviewedCC BYFull textEcologyDOAJ
BibTeX
@misc{sorboni2026much,
  title = {{How Much of the Forest Do Citizen Scientists Actually Record? A Decade of iNaturalist Sampling in the Hyrcanian Forests}},
  author = {Saman Ghasemian Sorboni and Motahareh Taghipour},
  howpublished = {EcoEvoRxiv},
  year = {2026},
  month = oct,
  doi = {10.32942/x2zx10},
  url = {https://ecoevorxiv.org/repository/view/15716/},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - How Much of the Forest Do Citizen Scientists Actually Record? A Decade of iNaturalist Sampling in the Hyrcanian Forests
AU  - Saman Ghasemian Sorboni
AU  - Motahareh Taghipour
PY  - 2026
DA  - 2026/10/29
PB  - EcoEvoRxiv
DO  - 10.32942/x2zx10
UR  - https://ecoevorxiv.org/repository/view/15716/
AB  - Citizen-science records increasingly inform biodiversity research and conservation planning, yet how much of a large forest they actually cover is rarely measured. We analyzed 1,700 research-grade iNaturalist records from the Hyrcanian Forest, northern Iran (2015–2025), on a 10 × 10 km grid, comparing observed coverage with null models. Annual records rose from 48 to 386, but occupied only 43.7% of forest cells, 41% fewer than an even spread would give, and 56% of cells had no record. Fifteen observers (top 10%) provided 69% of records. In a presence–background model with clus-ter-robust errors, only elevation was independently associated with record presence (odds ratio 0.92 per 100 m); distances to roads and settlements were not. Because steep terrain limits where people can walk and record, similar eleva-tion-related gaps may occur in other mountain forests, where record counts could overstate coverage. Models using such records should adjust for elevation bias.
ER  -