Submitted:
30 September 2024
Posted:
01 October 2024
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Abstract

Keywords:
1. Introduction
- (1)
- Investigate the impact of various iNaturalist user types on the quality and distribution of observations.
- (2)
- Evaluate the effectiveness of integrating iNaturalist observations and herbarium specimen data in biodiversity research.
- (3)
- Identify the challenges and limitations associated with both data sources, including issues of data quality and identification accuracy.
2. Materials and Methods
2.1. EcoFlora iNaturalist Projects
2.2. Herbarium Specimen Data
2.3. iNaturalist Data
2.4. Rare and Introduced Statuses
3. Results
3.1. iNaturalist Observations - “Research Grade” Insights
3.2. iNaturalist User Types
3.3. Case Studies
3.3.1. Rare Plant Wrangling
3.3.2. Finding Hay in a Haystack
3.3.3. Pokey Plant Problems
3.3.4. Emerging Invasives (Early Detection/Rapid Response)
3.3.5. Cultivated Curation
3.3.6. Tiny Feature Phenomenon
4. Discussion
- Real-Time Observations: Provides up-to-date information about species occurrences and distribution patterns.
- Public Engagement: Involves the public in scientific research, increasing awareness and appreciation of biodiversity.
- Large Dataset: Millions of observations provide a robust dataset for ecological studies.
- Geographical Coverage: Observations from all over the world, including underexplored regions, enhance geographical data diversity. Users do not need a permit to make an observation unlike for an herbarium specimen collection, thus increasing usability.
- Image Documentation: Photographs help understand species’ morphology across a geographic range. Users can upload multiple photographs to a single observation to aid in documenting morphology. Users can tag other organisms so that observations document species’ interactions.
- Accessibility: The platform is freely accessible to anyone with internet access, encouraging widespread use.
- Interactive Community: Provides a community-driven platform for users to discuss and validate observations.
- Educational Tool: Easy to use in educational settings to teach about biodiversity, ecology, taxonomy, and conservation.
- Financial: Less cost intensive to make observations than to house physical specimens.
- Data quality: More precise locations (unless coordinates are obscured). Because of their pinpointed geographic locality, iNaturalist observations are useful to track down specific plants or populations to add value to herbarium collections or for researchers looking for a specific species to voucher. These observations can be used to document new county records, fill in distribution gaps, make a collection of a distinctive phenotype, or as tissue vouchers for DNA extraction.
- Data Quality: Identification accuracy can vary due to non-expert contributors and community curation. Misidentifications can be difficult to overturn.
- Temporal Bias: More observations during certain times, like weekends or favorable weather conditions, can skew data.
- Spatial Bias: Overrepresentation in easily accessible areas may lead to uneven geographical data.
- Limited Historical Data: As a recent platform, it lacks long-term historical data compared to herbarium specimen data.
- Species Coverage Bias: More popular or charismatic species might be overrepresented in observations.
- Identification Challenges: Despite community validation, some observations may remain unverified because photographs lack visible characters needed to identify to species.
- Limited Environmental Context: Observations might lack detailed environmental or ecological context because users concentrate on providing detailed photographs instead of habitat information.
- Privacy Concerns: Users may choose not to share exact location data due to privacy concerns, resulting in imprecise locations. Vulnerable species that have locations automatically obscured on iNaturalist are not precise.
- Technological Limitations: Requires access to a smartphone or computer with an internet connection, which may not be available to all potential contributors.
- Non-physical Limitations: Photographs cannot be used for genetic research or examined closely for morphology.
- Scientific Reference: The longevity of photographs online is unknown. Digital assets could become lost if not properly backed up.
- Historical Data: Provides long-term records of species distributions and changes over centuries. Provides a baseline for studying changes in plant populations and distributions over time.
- Accurate Identifications: Physical specimens allow for thorough verification and taxonomic study. Many specimens have been examined/annotated by experts.
- Scientific Reference: Serve as a “permanent” scientific reference for future studies and species descriptions.
- Broad Range of Data: Each specimen can include detailed metadata, such as location, date, and habitat information.
- Genetic Studies: Specimens can be used for genetic analyses to understand evolutionary relationships.
- Physical Specimen: Allows for examination of plant morphology and anatomy over time. Preserved specimens allow users to dissect and examine parts under a microscope. Other features such as endophytic fungi, microbes, etc. may be specimens or attached to roots.
- Integration with Digital Tools: Digitization improves accessibility and data sharing. Photographs provide quick access to specimens without having to acquire specimens via a loan.
- Contributions to Conservation: Historical records aid in assessing species’ conservation status and habitat/landscape changes.
- Collaborative Research: Supports collaborative research through specimen loans and exchanges between institutions.
- Limited Temporal Coverage: May not represent current populations or distributions accurately because of lack of active collecting. May have underrepresented collections of common, widespread species.
- Taxonomic Bias: Specimens may have a taxonomic bias depending on the purpose of the collections and the collector.
- Resource Intensive: Collecting, storing, and maintaining specimens requires significant resources, finances, and expertise.
- Access: Physical access can be limited, although digitization efforts are improving.
- Geographical Bias: Collections may be biased towards regions with more historical collecting activity.
- Collection Bias: Historical collecting practices may have focused on certain species or habitats, leading to gaps in data.
- Physical Degradation: Specimens can degrade over time, especially regarding DNA quality, making them less useful for genetic studies. Pressed and dried specimens can lose some morphological features.
- Data Quality: Locations, particularly from older specimens, can be imprecise and vague. Prior to widespread GPS use, locality data relied on verbal descriptions or at best township, range, and section which plotted specimens to a one-mile square radius.
- Storage Challenges: Requires significant space and appropriate environmental conditions for storage. Can be difficult to acquire new collections if space is limited.
- Limited Public Engagement: Primarily used by researchers, with less direct public and student involvement compared to community science platforms.
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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| iNaturalist user type | Description |
|---|---|
| Influencer | Users that try to make as many observations as possible, often across multiple organismic groups. |
| Collector | Users that aim to collect as many unique species as possible. These observers also track down rare or uncommon taxa, or taxa with no previous photographs on iNaturalist. |
| Professional | Users that are professionals in the field, including herbarium/museum curators, botanists, or natural resource managers. Professionals often take and/or identify observations of specific organismal groups. |
| Casual | Users typically with 20 or fewer observations total, mostly of common taxa. These observations are often taken as part of class project or group outing. |
| New York | Denver | |
|---|---|---|
| Total observers | 12,447 | 11,962 |
| Total observations | 223,028 | 166,426 |
| Percent casual observers | 91% | 89% |
| Percent of observations from casual observers | 19% | 24% |
| Percent of observers with 10 or fewer observations | 84% | 81% |
| Percent of observers with a single observation ("supercasual") | 39% | 39% |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
