# Overcoming biodiversity blindness: Secondary data in primary citizen science observations

Nadja Pernat, Susan Canavan, Marina Golivets, Jasmijn Hillaert, Yuval Itescu, Ivan Jarić, Hjalte M R Mann, Pavel Pipek, Cristina Preda, David M Richardson, et al.

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► To cite this version:

Nadja Pernat, Susan Canavan, Marina Golivets, Jasmijn Hillaert, Yuval Itescu, et al. Overcoming biodiversity blindness: Secondary data in primary citizen science observations. Ecological Solutions and Evidence, 2024, 5 (1), pp.e12295. 10.1002/2688-8319.12295. hal-04407971

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Submitted on 21 Jan 2024

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PERSPECTIVE

# Overcoming biodiversity blindness: Secondary data in primary citizen science observations

Nadja Pernat¹,² ID | Susan Canavan³,⁴ ID | Marina Golivets⁵ ID | Jasmijn Hillaert⁶ ID |

Yuval Itescu⁷,⁸,⁹ ID | Ivan Jarić¹⁰,¹¹ ID | Hjalte M. R. Mann¹² ID | Pavel Pipek³,¹³ ID |

Cristina Preda¹⁴ ID | David M. Richardson³,¹⁵ ID | Heliana Teixeira¹⁶ ID |

Ana Sofia Vaz¹⁷,¹⁸,¹⁹ ID | Quentin Groom²⁰ ID

Correspondence

Nadja Pernat

Email: nadjapernat@uni-muenster.de

Funding information

European Cooperation in Science and
Technology, Grant/Award Number: COST
Action CA17122

Handling Editor: Mark O'Connell

Abstract

1. In the face of the global biodiversity crisis, collecting comprehensive data and making the best use of existing data are becoming increasingly important to understand patterns and drivers of environmental and biological phenomena at different scales.

2. Here we address the concept of secondary data, which refers to additional information unintentionally captured in species records, especially in multimedia-based citizen science reports. We argue that secondary data can provide a wealth of ecologically relevant information, the utilisation of which can enhance our understanding of traits and interactions among individual organisms, populations and biodiversity dynamics in general.

3. We explore the possibilities offered by secondary data and describe their main types and sources. An overview of research in this field provides a synthesis of the results already achieved using secondary data and different approaches to information extraction.

4. Finally, we discuss challenges to the widespread use of secondary data, such as biases, licensing issues, use of metadata and lack of awareness of this trove of data due to a missing common terminology, as well as possible solutions to overcome these barriers.

5. Although the exploration and use of secondary data is only emerging, the many opportunities identified show how these data can enrich biodiversity research and monitoring.

KEYWORDS

artificial intelligence, biodiversity, citizen science, community science, ecological interactions, invasion science, monitoring
# 1 | THE UNTAPPED INFORMATION IN EXISTING BIODIVERSITY DATA

Citizen science contributes enormously to biodiversity monitor-
ing (Chandler et al., 2017) by providing data that are potentially
as useful as those collected by professional scientists, especially
for research over large spatial and temporal extents (Callaghan
et al., 2020). However, there are still major gaps in the taxonomic,
temporal and spatial coverage of biodiversity data to track changes
in species' abundance and distribution (Amano et al., 2016; Feldman
et al., 2021; Wetzel et al., 2018). Furthermore, biodiversity encom-
passes not only the diversity of organisms but also the diversity of
interactions between them and with their environment, which has
been given scant attention by citizen science (Chandler et al., 2017;
Groom et al., 2021). Ecological interactions are the foundation of
ecology and the architecture of ecosystems, but observing these
relationships is challenging (Jordano, 2016). The sheer amount and
complexity of interactions as well as the detection probability in the
field limits studies with respect to number of species, locations and
time periods that can be studied.

However, both occurrence and interaction data are needed to
understand, address and mitigate the consequences of the five
major drivers of biodiversity loss (Díaz et al., 2019). For instance,
land-use change may affect host-vector dynamics (Spence Beaulieu
et al., 2019), pollution may lead to adaptations in species traits (Rech
et al., 2022), climate change can affect trophic cascades and dis-
tribution shifts (van Gils et al., 2016), overexploitation may change
former mutualisms (Speziale et al., 2018) and biological invasions
can result in novel plant-pollinator networks (Parra-Tabla & Arceo-
Gómez, 2021). The variety of direct and indirect effects of biotic and
abiotic interactions are difficult to study, which poses a challenge
that can only be addressed through global collective effort (Díaz
et al., 2019).

To achieve more accurate and reliable monitoring and inter-
action data, we need to improve and integrate current methods
(Besson et al., 2022; Kühl et al., 2020; van Klink et al., 2022), to bet-
ter analyse existing data (Probert et al., 2022), and to extract more
information from already collected data (Johnston et al., 2022).
In the latter case, one untapped source of abundant data is the
corpus of digital images and other media, generated and shared
on citizen science platforms. As of May 2023, four such websites
alone (Artportalen, iNaturalist, Observation.org, and Pl@ntNet)
had collectively published over 78 million images through the
Global Biodiversity Information Facility (GBIF). Almost all these
photos were taken of organisms or signs of their presence in situ
(i.e. nests, faeces, tracks, etc.) and thus may capture ecologically
relevant information as a by-product. This type of additional infor-
mation has been termed 'secondary data' (Callaghan et al., 2021,
see Box 1 for details). Having recognised the potential, a grow-
ing number of studies have examined ecological questions using
incidentally captured information. Yet, so far, the scientific com-
munity has been relatively blind to the opportunities to extend

BOX 1 What are secondary data?

Secondary data in the context of citizen science in biodi-
versity research, as we define it, refers to a subset of infor-
mation that is unintentionally captured alongside primary
data. Primary citizen science data collected for a specific
research focus, such as monitoring the distribution of a
species, provide information on the location and date of
record of the species in addition to evidence of its occur-
rence. This primary data of ‘what?’, ‘when?’ and ‘where?’
are the intended focus of many ad hoc observing portals. In
contrast, secondary data are ancillary details that are also
present in the materials collected but were *not the intended
subject* of the study. Indeed, the observer may have been
unaware of the secondary information they evidenced.

Secondary data can offer valuable opportunities for addi-
tional research and analysis, enriching our understanding
of ecosystems' functioning, population dynamics, natural
behaviours and environmental conditions. They represent
any retrievable pieces of information that can be seen on
an image or in a video, heard on an acoustic recording or be
included in a descriptive text. The information they con-
tain may relate to features of individuals or populations,
biotic interactions (including human-nature interactions),
landscape and environmental conditions, or any other bi-
otic or abiotic features or their combination (Figure 1). We
recognise, of course, that the subject of investigation may
be something other than the mere detection of species. In
complex citizen science programmes, the primary data can
only be separated from secondary data with reference to
the objectives of the project. For example, in the COASST
project (Parrish et al., 2017), citizen scientists record bird
carcasses on beaches, providing not only an image but also
record a wealth of information about the morphology of
the carcass and the state of the environment. In addition
to the primary information collected, the images of dead
birds may contain even more information than the research
project anticipated, such as the presence of necrophagous
species. Thus, secondary data are data that the methodol-
ogy was not intended to capture, though there are no sharp
demarcation lines.

our knowledge of biodiversity from secondary data. Therefore, we
assume a 'biodiversity blindness', drawing on the concept to de-
scribe the public's lack of attention to the presence and diversity
of plant and animal life (Moscoe & Hanes, 2019).

Following our definition of secondary data (Box 1), we do not
explicitly refer to the metadata (e.g. timestamp or geolocation) as-
sociated with the occurrence record, though they also represent
potential secondary data sources and play a major role in selecting
<figure><img src="image_3.png" /><figcaption>FIGURE 1 Examples of secondary data captured in images vouchering for species occurrences. (a) background habitat of a fire salamander (Salamandra salamandra), and the unintended record of another species (the fungus) (b) the interaction between human and red fox (Vulpes vulpes), (c) interaction between a leaf miner and the host plant, (d) predation by the Mexican grass-carrying wasp *Isodontia mexicana*. Image copyrights: © https://www.inaturalist.org/photos/251871116, http://creativecommons.org/publicdomain/zero/1.0/ (a) © juniper_likethetree https://www.inaturalist.org/observations/155228701, https://creativecommons.org/licenses/by/4.0/ (b) ©ahabo https:// www.inaturalist.org/photos/257468598, https://creativecommons.org/licenses/by/4.0/ (c) © Mattia Menchetti https://www.inaturalist.org/ observations/129293402, https://creativecommons.org/licenses/by/4.0/ (d).</figcaption></figure>

datasets and their analyses. An important element in utilising sec-
ondary data is the explorative character of this research method, as
the nature of this unintentionally recorded information may be un-
clear. The additional information may document biotic interactions
and co-occurrences, which could provide not only important ecolog-
ical information of the observed species but also records of the by-
catch for respective monitoring programmes. Secondary data may
also include details on morphology, behaviour, habitat and various
other aspects of a species' traits and ecology.

This paper explores the opportunities and pitfalls of extracting
secondary data from multimedia records of biodiversity. We also
examine how advances in artificial intelligence can and might accel-
erate data extraction. Our goal is to illuminate the hidden treasure
of biodiversity data contained in citizen science multimedia records
and available openly to the scientific community. While the efforts
to explore and exploit the realm of secondary data are still in their
infancy, they already demonstrate numerous opportunities to enrich
and inform biodiversity research.

2 | RESEARCH OPPORTUNITIES AND TYPES OF SECONDARY DATA

Extracting secondary data from existing citizen science sources
helps to address universal challenges of biodiversity research, such

as taxonomic bias, detectability of species and their interactions,
and recognition of spatio-temporal dynamics.

Taxonomic bias towards charismatic or well-known species in citizen science data poses a challenge for researchers and limits the possibilities of launching projects that deal with less popular, cryptic or under-researched species. In addition, although simple, unstructured programmes generate high numbers of citizen science observations, information that is more complex to record, such as an individual's health condition, is not purposefully collected and therefore not formally documented. Similarly, studies that are less engaging, such as those that are time-consuming, physically challenging or in less attractive localities are discriminated against. Extracting secondary data from existing observations could be fruitful to fill such data needs. For example, diurnal or seasonal activity patterns or vocal characteristics of rare species could be retrieved from soundscapes or the background of audio recordings of focal species. From images, occurrences of less charismatic arthropods or pathogenic fungi living on photographed plants could be extracted. In the latter case, in 2010, citizen scientists monitored and scored leaf damage on horse-chestnut trees (*Aesculus hippocastanum*) caused by the leaf-miner *Cameraria ohridella* in Great Britain (Pocock & Evans, 2014). Today, the infestation could also be detected as secondary data in images on which horse-chestnut trees are the primary observation, thereby improving the data situation at low additional costs and resource expenditure.
Using secondary data to take advantage of the taxonomic bias towards well-documented species also brings additional research opportunities. When observations for a given species are widely available, one can extract data on multiple aspects of interest, such as morphological traits or biotic interactions, without spending time and resources on launching and running a new raw data collection campaign. For example, Putman et al. (2021) used images of the secretive but thoroughly documented lizard *Elgaria multicarinata* that were primarily collected for determining the species' distribution in Southern California. From the images, the authors assessed predation pressure and health condition by measuring the lizards' tails and by looking for ectoparasites in the animal ear regions. Likewise, citizen science photos have been used to identify subtle morphological differences between two very similar species of grasshopper and to establish their distributions (Pélissié et al., 2023).

Coincidental evidence can mitigate low detection probabilities. By identifying a rare species in primary observations of other species, whether that is through biotic interaction or an incidental co-occurrence, the pool of observations can be enlarged. A citizen science project in Australia has shown that co-occurrence of a common and rare possum species can lead to more detections of the latter (Steven et al., 2021). Aside from potentially increasing sample sizes of monitoring data for the benefit of statistical analyses, we can improve our understanding of ecological impacts on other species, including people. We envisage application in pollination dynamics, invasion impact or climate change research. For example, we can potentially study the preferred flower species and colour in a network of native and exotic bumblebees and host plants (Catron et al., 2023; Fontúrbel et al., 2023). Another example is hair loss in moose (*Alces alces*) and wapiti (*Cervus canadensis*) caused by the expanding distribution of winter tick

(Dermacentor alipictus) due to climate warming in Yukon, Canada (Chenery, 2023). Serendipity is a factor as well to reveal ecological interactions; Rosa et al. (2022) not only found new and supposedly extinct species as primary observations, but also novel predatory interactions that were accidentally captured in the iNaturalist images of marine snails. Such chance discoveries based on the background information could be especially useful in invasion science, where secondary data may reveal new or hidden invasions or previously undocumented ecological processes that facilitate or hinder invasions.

Extracting secondary data from a series of observations across space and time can also support efforts to move from a mere single-species snapshot (an occurrence record) to spatio-temporal biodiversity dynamics. Using timestamps and geolocation metadata of citizen science observations to investigate spatial and temporal dynamics has been successfully applied before (e.g. Feldman et al., 2021; Newson et al., 2016). Given a sufficient temporal span and frequency of observations, we suggest linking secondary data to such a stamp to obtain a variety of observable dynamics. For example, a series of landscape images would not only contribute to monitoring data (e.g. the abundance and distribution of species on the images), but can also be useful for studying phenological dynamics at the community level (Hofmeester et al., 2020).

Figure 2 illustrates how secondary data can add contextual dimensions to primary species observations, thereby mitigating ‘biodiversity blindness’ by expanding on the information in citizen science multimedia records beyond geographical locations and time. This context applies to different scales and scopes of consideration, specifically on the level of individuals, populations, communities, the surrounding environment and the human dimension. For each of these levels, Table 1 gives extensive lists of types of information contained in secondary data.

<figure><img src="image_4.png" /><figcaption>FIGURE 2 Illustrative relationship between primary and secondary data as sources for different types of information. Image copyright: © zilpzalp17, https://www.inaturalist.org/observations/148103702, https://creativecommons.org/licenses/by-nc/4.0/.</figcaption></figure>
<table><caption>TABLE 1 Types of information extractable from secondary data from citizen science projects. As the literature on secondary data is scarce and obscure due to a missing common terminology, we also listed examples that used secondary information in combination with other sources and approaches (e.g. iEcology or literature). The table groups the publications by level (human-nature interactions, features of the individual, etc.) as described. For each study it gives the feature of interest (extracted data) and the data elements used to extract it as well as a short description (study example) of the content and the methods and sources used (source and extraction method).</caption><thead><tr><th>Data element</th><th>Extracted data</th><th>Study example</th><th>Source and extraction method<sup>a</sup></th><th>Reference</th></tr></thead><tbody><tr><td colspan="5"><strong>Observer/human-nature interactions</strong></td></tr><tr><td>Interpretation of photo</td><td>Animal behaviour</td><td>Human-coyote encounter in urban environments, assessing timing, level of urbanisation and aggressiveness</td><td>iNaturalist; manual extraction by visual inspection and categorisation of coyote behaviour</td><td>Drake et al. (2021)</td></tr><tr><td>Interpretation of photo</td><td>Association with human infrastructure</td><td>Investigations of wrens nesting in human stable or unstable structures by examining citizen science images</td><td>iNaturalist, eBird, WikiAves and field data; manual extraction by visual inspection of nesting sites</td><td>Alexandrino et al. (2022)</td></tr><tr><td>Interpretation of photo</td><td>Cause of death</td><td>Taxa and seasonality of roadkills in the USA</td><td>Tagged iNaturalist images ('roadkill', 'dead on road'); manual extraction by visual inspection, for example, species identification</td><td>Unger (2022)</td></tr><tr><td colspan="5"><strong>Features of the individual</strong></td></tr><tr><td>Time and interpretation of photo</td><td>Diurnal patterns</td><td>Diurnal pattern of a lizard</td><td>iNaturalist, HerpMapper; manual extraction by visual inspection of daytime and light on the images</td><td>Blais and Shaw (2018)</td></tr><tr><td>Date and interpretation of photo</td><td>Phenology</td><td>Fine-scale delimitation of the flowering phenology of Yucca species</td><td>iNaturalist; manual extraction by applying a scoring rubric and consensus method</td><td>Barve et al. (2020)</td></tr><tr><td></td><td></td><td>Progression of phenological stages of Alliaria petiolata</td><td>iNaturalist; deep learning algorithm to classify phenological stages</td><td>Reeb et al. (2022)</td></tr><tr><td>Identification and interpretation of photo</td><td>Hybrids</td><td>Assessing hybridisation by colour variation among grass snakes across Europe</td><td>iNaturalist; manual extraction by visual inspection following an identification key for several coloured features</td><td>Fritz and Ihlow (2022)</td></tr><tr><td>Measurement and interpretation of photo</td><td>Morphological traits</td><td>Pigmentation of wings in male damselflies across broad spatial scales<br>Melanisation of grey squirrels in relation to urbanisation and temperature<br>Length ratio measurements to identify two closely related grasshopper species</td><td>iNaturalist; half-automated extraction of size and place of pigmentation using visual inspection and ImageJ<br>iNaturalist and SquirrelMapper; manual extraction of squirrel colour by visual inspection in the form of a citizen science project<br>iNaturalist; length ratios from photos using ImageJ</td><td>Drury et al. (2019)<br>Cosentino and Gibbs (2022)<br>Pélissié et al. (2023)</td></tr><tr><td>Interpretation of photo</td><td>Behavioural traits</td><td>Warming-up behaviour of differently coloured rattlesnakes and their corresponding survival rates</td><td>iNaturalist; manual extraction and classification of colour, living or dead and background</td><td>Rhodes et al. (2022)</td></tr><tr><td>Date and interpretation of photo</td><td>Life history</td><td>Breeding biology of swallow-tailed hummingbirds</td><td>WikiAves, iNaturalist, eBird; manual extraction of classes of breeding stages (nesting, eggs, fledglings, etc.) and using dates for calculation of breeding timings</td><td>Turella et al. (2022)</td></tr><tr><td>Interpretation of photo</td><td>Health</td><td>Body condition of seals</td><td>Applying images of the New Zealand Leopard Seal Photograph Library (NZLSPL) to a self-developed scoring system</td><td>Warren (2021)</td></tr></tbody></table>

(Continues)

<figure><img src="image_5.png" /></figure>
<table><caption>TABLE 1 (Continued)</caption><thead><tr><th>Data element</th><th>Extracted data</th><th>Study example</th><th>Source and extraction method<sup>a</sup></th><th>Reference</th></tr></thead><tbody><tr><td>Measurement and interpretation of photo</td><td>Injuries/damage</td><td>Frequency of wing damage in migrating butterflies</td><td>Comparing images of butterfly wings with a reference image and estimating frequency/coverage of damage using Inkscape and MATLAB</td><td>Korkmaz et al. (2022)</td></tr><tr><td colspan="5"><strong>Biotic interactions and co-occurrence</strong></td></tr><tr><td>Identification</td><td>Flower visitation/ pollination</td><td>Identification of host flowers of the introduced wasp <i>Isodonta mexicana</i> with an iNaturalist-PI@ntnet-workflow</td><td>iNaturalist; automatic data exchange tool approach validated by visual inspection</td><td>Pernat et al. (2022)</td></tr><tr><td>Identification and interpretation of photo</td><td>Species conflicts</td><td>Competition for nest cavities between bee species and wild honey bee colonies in its native and introduced ranges</td><td>iNaturalist and literature review; from images manual extraction of country, combs and entrance characteristics of nests</td><td>Saunders et al. (2021)</td></tr><tr><td>Identification and interpretation of photo</td><td>Diet</td><td>Analysing prey items in correlation with the age and sex of sparrowhawks by images from different sources</td><td>Google Images, Macaulay Library, iNaturalist, BirdGuides, Facebook, and Twitter by automatic or manual retrieval; extraction of sparrowhawk age, sex, and prey species by visual inspection</td><td>Panter and Amar (2021)</td></tr><tr><td>Identification and interpretation of photo</td><td>Host preferences</td><td>Host preferences and phenological peak of hairworms</td><td>iNaturalist and literature review; manual extraction by visual inspection of infected host species</td><td>Doherty et al. (2021)</td></tr><tr><td>Identification of photo</td><td>Disease</td><td>The proportion of <i>Caryophyllaceae</i> plants infected with anther-smut disease in eastern USA, comparing herbarium and citizen science data</td><td>iNaturalist and herbarium data; manual extraction and classification—infected or not—by visual inspection</td><td>Kido and Hood (2020)</td></tr><tr><td>Identification and interpretation of photo</td><td>Predation</td><td>Using images to investigate the spectrum of prey of assassin bugs (Hemiptera: Reduviidae)</td><td>Flickr, iSpot Nature, BugGuide, NatureWatch, Google Images; extraction of predation events and preyed species by visual inspection</td><td>Hernandez et al. (2019)</td></tr><tr><td>Measurement and interpretation of photo</td><td>Parasitism</td><td>Assessing parasitism and predation of lizards in an urban environment by measuring tail/ body ratio and number of ticks</td><td>iNaturalist, HerpMapper; manual extraction of tick number by visual inspection, half-automated extraction of relative body size with measurement via ImageJ</td><td>Putman et al. (2021)</td></tr><tr><td>Measurement, identification and interpretation of photo</td><td>Basibionts</td><td>Occurrence and environmental conditions of epibiotic algae on gastropods detected from images of a corresponding citizen science project in Japan</td><td>iNaturalist, manual extraction of the proportion of infected fish species by visual inspection</td><td>Happel (2019)</td></tr><tr><td>Identification and interpretation of photo</td><td>Cause of death</td><td>Quantity and causes of shark stranding on a global scale</td><td>Dedicated citizen science project collecting images via Google Form, e-mail and Twitter; manual extraction of substrate and condition by visual inspection, coverage of epibiotic algae measured with ImageJ</td><td>Kagawa et al. (2020)</td></tr><tr><td colspan="5"><strong>Features of the population and community</strong></td></tr><tr><td>Measurement and interpretation of photo</td><td>Mating system</td><td>Evolution of mating-related wing ornamentation of dragonflies influenced by climate warming</td><td>Literature, iNaturalist, YouTube, Twitter, Facebook, Instagram; manual extraction of species, sex and potential cause of stranding</td><td>Wosnick et al., 2022</td></tr><tr><td></td><td></td><td></td><td>iNaturalist; half-automated extraction of wing pigmentation with ImageJ (following Drury et al., 2019)</td><td>Moore et al. (2021)</td></tr></tbody></table>
<table><caption>TABLE 1 (Continued)</caption><thead><tr><th>Data element</th><th>Extracted data</th><th>Study example</th><th>Source and extraction method<sup>a</sup></th><th>Reference</th></tr></thead><tbody><tr><td>Quantification and identification</td><td>Population size</td><td>Decline of introduced llama population in Italy estimated by recognition of individuals on images</td><td>iNaturalist, Facebook, Twitter; manual extraction of coat pattern and age (by body size) by visual inspection</td><td>Gargioni et al. (2021)</td></tr><tr><td>Quantification and identification</td><td>Sex ratio, age groups</td><td>Sex ratio and proportion of life stages of manta rays</td><td>images of Manta-Matcher (citizen science) combined with YouTube, Facebook, Instagram, Flickr and Vimeo and private collections; extraction of sex and life stages by visual inspection</td><td>Knochel et al. (2022)</td></tr><tr><td>Environmental features</td><td></td><td></td><td></td><td></td></tr><tr><td>Interpretation of photo</td><td>Habitat type</td><td>Classification of marine biotic habitats from image background and evaluating results with professional reef life survey</td><td>iNaturalist; manual extraction and classification of biotic habitats by visual inspection</td><td>Bolt et al. (2022)</td></tr><tr><td>Interpretation of photo</td><td>Cause of death</td><td>Correlating weather and catastrophic events with images of dead birds</td><td>iNaturalist project dedicated to bird mortality</td><td>Yang et al. (2021)</td></tr><tr><td>Interpretation of photo and location</td><td>Substrate</td><td>Substrate choices of native oyster species<br>Ostrea lurida</td><td>iNaturalist, fieldwork and literature; manual extraction of the substrate information by visual inspection of images in combination with observer notes and Google Earth</td><td>Kornbluth et al. (2022)</td></tr></tbody></table>

## 3 | SECONDARY DATA ARE SLOWLY DIFFUSING INTO THE SCIENTIFIC LITERATURE

Studies using secondary data (Table 1) have mostly focused on the extraction of morphological information, such as the pigmentation on wings of Calopterygidae damselflies (Drury et al., 2019), coloration patterns of grass snakes (Fritz & Ihlow, 2022), and intra- and interspecific variabilities in coloration of birds and plants (Laitly et al., 2021). Some studies also used secondary data to assess human-nature interactions such as bat handling during the COVID crisis (Van der Jeucht et al., 2021), to classify marine habitats using image backgrounds (Bolt et al., 2022), and to identify plants visited by hummingbirds (Marín-Gómez et al., 2022). Secondary data from citizen science are often combined with iEcology or culturomics data sources (Jarić et al., 2020) or museum collections (Box 2). Examples include a dietary study of African snakes (Maritz & Maritz, 2020), arthropod parasitism by hairworms (Doherty et al., 2021) and the distribution of anther-smut disease in the *Caryophyllaceae* plant family (Kido & Hood, 2020).

Citizen science has already proven useful in mapping and tracking biological invasions (Encarnação et al., 2021). The additional information that comes with secondary data could reveal even more aspects of the invasion process, thereby supporting invasive species management. For example, first approaches explored the host plants of introduced pollinators (Bila Dubaïć et al., 2022; Guariento et al., 2019; Pernat et al., 2022) and cavity occupancy by wild honey bees (*Apis mellifera*) in Australia (Saunders et al., 2021).

Reanalysis of images has also been used for trait-based studies to characterise, for example particulate matter in the global oceans (Trudnowska et al., 2021) and the feeding habits of marine copepods

## BOX 2 Secondary data, conservation culturomics and iEcology—What is the difference?

The use of secondary data in research is similar to the emerging areas of conservation culturomics and iEcology (Jarić et al., 2020). Culturomics seeks to understand human culture through the quantitative analysis of changes in word frequencies in large bodies of digital texts (Michel et al., 2011). In the context of biodiversity, the emergent area of ‘conservation culturomics’ focuses on the relationship between people and nature (Ladle et al., 2016), informed by contents of various types of online data. iEcology, on the other hand, is an umbrella term for analysing various types of digital data generated or collected for purposes other than ecological research to obtain insights into ecological questions. In contrast, in citizen science projects, people consciously contribute to the goal of a particular activity, such as biodiversity monitoring or invasive species detection (Marchante et al., 2023).

<footnote><sup>a</sup>Only the sources and methods used to obtain secondary data whose data type was the subject of the study (feature, interaction, etc.) are listed. In most cases, the analyses also used geo-locations, dates and metadata that were part of the primary observation.</footnote>
(Vilgrain et al., 2021), although these studies did not use citizen science data. The potential for extracting functional traits from images, either directly measured or inferred by combining visible features with context metrics from the metadata, has been thoroughly considered for plankton (Orenstein et al., 2022).

## 4 | SECONDARY DATA EXTRACTION COULD BE ACHIEVED ALONG A GRADIENT OF HUMAN AND ARTIFICIAL INTELLIGENCE

As approaches to obtain secondary data are just emerging, such data are still mainly extracted manually. This can be challenging when thousands of images need to be interpreted and evaluated. For example, the aforementioned study of anther-smut infection within the *Caryophyllaceae* examined 79,801 iNaturalist images (Kido & Hood, 2020). There is much to be gained from automation that could scale up the process to millions of images, particularly for pre-selecting images and recognising relevant image features. For example, computer vision could be used to extract and analyse information on colour in images, for example, greenness of plants (Yuke, 2019), and deep learning models to detect, count and classify specific features of interest (Bjerge et al., 2023; Mann et al., 2022). Likewise, algorithms and pretrained dictionaries in Natural Language Processing could leverage the use of textual content, such as image captions, commentaries and tags in secondary data. Automated systems would also facilitate real-time analysis of biodiversity dynamics, making them particularly useful for informing decision-makers regarding effects of conservation efforts or as early warning tools (van Klink et al., 2022).

Despite the obvious appeal of machine learning for automatic data extraction from citizen science sources, several obstacles lie before its full potential can be realised. Developing robust models that effectively handle diverse and noisy datasets is challenging and resource-intensive. Nevertheless, for some tasks, existing tools may be customised or applied directly. Multiple trained deep learning models to screen multimedia for human or natural objects are freely available. For instance, object detection models, which are often pretrained and benchmarked on the COCO dataset (Lin et al., 2014) containing 80 different object categories (including birds and other animals), may already provide relevant secondary data output. Moreover, models exist for specific groups of organisms and data types: Merlin Bird ID and BirdNET (Kahl et al., 2021) for bird detection based on sound (the former can identify species also from images), Pl@ntNet API for plants, Bjerge et al. (2023) created a test dataset for insects, FishID for fish species in images; MegaDetector or TrapTagger for animals in camera trap photos; and BatDetect2 and BatNet for bats (Aodha et al., 2022; Krivek et al., 2023) in sound recordings. Additionally, customised models can be trained on open datasets, for example, FathomNet for marine organisms (Katija et al., 2022), Pl@ntNet for plants and iNaturalist for a range of different species. Importantly, even with readily available models, manual resources and expertise are

required to ensure the anticipated model behaviour and performance on new data.

In other cases, models and analysis pipelines may need to be developed from scratch. Where models or training data are not available, the cost-benefit ratio of developing new artificial intelligence models should be weighed against the use of human-mediated approaches. For example, Mann et al. (2022) developed an approach to automatically detect flowering plants in images, which were then examined by citizen scientists for the rare presence of insects.

Efficient processing is relevant when dealing with large amounts of data, but it is critical to consider the resources needed. Developing custom automated methods and their broader usefulness and applicability versus setting up and maintaining manual processing pipelines (e.g. citizen science projects or recruiting and managing volunteers) may differ in terms of time, costs and personnel demands as well as the output quality. In any case, to address the uncertainty in exploratory analyses of secondary data variables, that is, to get an idea of what kind of additional information primary datasets contain, a subset of data would most often be analysed manually. This pre-processing can inform researchers about which methodologies to apply for larger scale extraction of information.

We expect that future secondary data extraction will be performed on a continuum between fully human and fully automated approaches with the respective advantages and disadvantages along this spectrum. Hybrid intelligence, that is, the combination of deep learning and human diligence (Mann, 2022; Rafner et al., 2021), can be effectively used to extract and analyse secondary data. Primary data can be filtered manually and, if necessary, annotated or immediately tested for relevant secondary data in the case of an existing algorithm. Conversely, one or more features can be selected from images (or other types of media) by an algorithm to be processed afterwards by a human (e.g. for annotation, validation or analysis; Figure 3).

Another challenge to apply artificial intelligence in secondary data studies is not knowing which data variables to look for and how to select or develop a potential identification algorithm or, simply put, how to search for the unknown unknowns. A human eye is able to identify the unexpected while the algorithm only recognises what is expected of it, that is, what it was trained for. In order to leverage the power of artificial intelligence for effective data extraction, data collection generally needs to be guided by precise research questions and must be based on a priori identification of variables of interest. As detection models trained to recognise an increasing number of objects, or segmentation models able to distinguish different areas in images (Kirillov et al., 2023) emerge, these may become increasingly relevant for exploration of secondary data without predetermined knowledge on what to look for. As artificial intelligence tools develop to analyse, visualise and synthesise multi-modal data (audio, video, text, etc.), it is likely that they will be able to recognise multiple features of interest. Also, identifying patterns across media types, including insights overlooked by a researcher, and even suggesting mechanisms and hypotheses that could explain such patterns are within reach. Application of these models should
<figure><img src="image_6.png" /></figure>

FIGURE 3 Interaction and possible applications of methods to extract/ retrieve secondary data. Our sample image of the primary observation of a raptor (a) also happened to include a black woodpecker and two plant species as secondary data (b). These exemplary secondary data could be extracted by humans or artificial intelligence only or by both in hybrid intelligence approaches (c). (image: © zilpzalp17, https://www. inaturalist.org/observations/148103702, https://creativecommons.org/licenses/ by-nc/4.0/).

<figure><img src="image_7.png" /></figure>

always be done with careful consideration of potential model biases that can skew results.

5 | WHY ARE WE NOT THERE YET?

Citizen science multimedia records are clearly more than meets the eye. To protect biodiversity, it is not only essential to inventory and monitor species, but also to understand the ecological networks they are part of. By giving many examples of current and possible future areas of application we demonstrated how secondary data offer the opportunity to extend and complement systematically collected interaction and monitoring data. Although we are convinced of the great potential in the untapped information, we still see some challenges to overcome and specific pitfalls to address.

Similar to the early days of the citizen science movement, the issue of bias can cast doubt on this new resource. Indeed, we suspect a similar bias in secondary data as in primary data (Isaac et al., 2014). Secondary data, however, would be less influenced by known recording behaviour (e.g. aesthetic preferences or charisma of observed target species) and more affected by previously less considered human actions. Staging of observed species in a particular location and environments, and cultural differences in what can be appropriately photographed are imaginable examples. In these

cases, scientists using secondary data can benefit from accelerated development and discussions in analysing opportunistic data to correct for bias (Johnston et al., 2022). Transparent handling of potential biases should be a given in both metadata and corresponding publications.

Of greater concern are biases from data generated by citizen science projects with unknown scientific goals. For example, when for a project citizens document a particular plant solely in forest habitat, higher-level analysis of that plant's habitats based on image backgrounds would document an unrepresentative proportion of this plant in forests. Therefore, the source of data should be known, that is, in unclear cases, the project organisers would also need to be contacted or the images excluded from the analysis. A thorough critique of data provenance may be particularly necessary if counterintuitive trends or patterns emerge during initial spatio-temporal visualisation or classification of data.

Major obstacles preventing the breakthrough of secondary data research concern methods for filtering and processing the vast amounts of data. Filtering primary data for the desired information is mainly a matter of metadata. More precise information provided by metadata for each image, text file, audio recording and other data types can speed up data acquisition and minimise errors. Citizen science platforms such as iNaturalist allow users to add more details about their records in corresponding observation fields or select
specific features from the list of annotations. However, free-text
fields are arbitrary, and feature lists are confining, and may not in-
clude, for example, possible interactions between species.

Since improving metadata according to the FAIR (Findability,
Accessibility, Interoperability, and Reusability) principles is a world-
wide effort, this problem will hopefully be solved with time. Similarly,
the development of new and better machine-aided object recog-
nition will allow large amounts of secondary data to be processed
automatically in the future. In addition, approaches have been devel-
oped to not only recognise objects in multimedia data sources, but
also to differentiate by anomalies or other species-specific features
such as plant or animal colours (e.g. Hantak et al., 2022; Perez-Udell
et al., 2023). Ultimately, the ever-improving models used to generate
ecological networks also help to turn information into knowledge.

A more pressing issue is the legality and ethical defensibility of
using millions of secondary data sources for purposes other than
those intended when the primary observation was recorded and
posted. Considerations of ethical and privacy issues are not exclu-
sive to secondary data. They are also pertinent regarding the pri-
mary data used in iEcology and culturomics (Jarić et al., 2020), from
which secondary data can be derived. While much of primary data
(e.g. online texts, images, videos or audio recordings) are publicly
available, and in many cases, people have given consent to their
availability (e.g. by registering in citizen science or social media
platforms), researchers are required to pay careful consideration to
how they collect, use and share these data (Di Minin et al., 2021;
Thompson et al., 2021; Zimmer, 2010).

Ethics are particularly relevant when dealing with online data
from social media, where work is often used or distributed without
the owners' consent. In fact, most social media platforms allow post-
ing nearly any content, as they are not able to automatically identify
copyrighted material. This issue is less prominent on citizen science
platforms, as the users have stronger control of posted data and
media licensing. But the way such information is shared and scraped
still opens various possibilities of copyright infringement in the dig-
ital space. As such, there is a considerable uncertainty regarding
situations in which acquiring permission and crediting authorship
becomes mandatory. If not Creative Commons, checking licences
can become a time-consuming process. Especially, when dealing
with big data that are derived from multiple sources, it may be highly
unfeasible to directly contact the media owners to get permission
for use (Leighton et al., 2016).

Ethical issues have to be carefully considered and are especially
delicate when secondary data allow recognition of people, or allow
the identification of contentious human interactions, such as illegal
fisheries (Sbragaglia et al., 2021), poaching or trade in wild organisms
(Di Minin et al., 2019; Zimmer, 2010). Di Minin et al. (2021) have
suggested a set of guidelines that can help address ethical concerns
in research when using such data. Likewise, while publicly sharing
species location information is useful for research, disclosing the
location and identification of rare or threatened species can be-
come a threat to their conservation (Lindenmayer & Scheele, 2017).
Although citizen science platforms such as iNaturalist already

consider 'taxon geoprivacy' as a way to safeguard the locations of
species 'at risk', a sensitive species as secondary data would still
come with full coordinates if not recognised as such.

Finally, it is most important that awareness of the existence and
potential of secondary data grows among scientists. With our con-
tribution, we aim to open the eyes of the scientific community to
overcome 'biodiversity blindness' and acknowledge the wealth of
information far beyond the location and date of a species observa-
tion in the millions of freely available multimedia files. Besides being
blind to this treasure of data, studies and projects dedicated to the
topic may also not be seen as such due to a lack of common termi-
nology. Therefore, we would like to establish the term secondary
data as proposed by Callaghan et al. (2021) or at least stimulate a
discussion about terminology, so that a corresponding field of re-
search can grow.

It should be clear to the community that this approach applies
not only to data from citizen science, social media or webpages, but
also to data collected by scientists in the field or in the laboratory.
The multiple benefits demonstrated here should convince people to
make the (raw) data available to the public according to the FAIR
principle, be it via GBIF, GitHub or other openly accessible reposi-
tories. As with all new and innovative methods, a transition period
will be necessary before this approach is fully integrated into the
research toolkit. Again, we draw comparisons with iEcology, cultu-
romics and citizen science in that secondary data are utilised in a
complementary and supportive way to other data sources and veri-
fied with ground truthing. Efforts to explore and use secondary data,
although still in their early stages, are already demonstrating many
ways to enrich and inform biodiversity research.

AUTHOR CONTRIBUTIONS

Nadja Pernat conceived the ideas and planned and facilitated a 3-
day workshop in November 2022 that was attended by all authors;
Nadja Pernat, Yuval Itescu, Jasmijn Hillaert, Cristina Preda and
Marina Golivets researched the reviewed articles; Nadja Pernat,
Jasmijn Hillaert and Susan Canavan created the visualisations; Nadja
Pernat, Quentin Groom and David M. Richardson led the writing of
the manuscript. All authors contributed critically to the drafts and
gave final approval for publication.

AFFILIATIONS

<footnote n="1">¹Institute of Landscape Ecology, University of Münster, Münster, Germany;</footnote>

<footnote n="2">²Centre for Integrative Biodiversity Research and Applied Ecology (CIBRA), University of Münster, Münster, Germany; ³Institute of Botany, Czech Academy of Sciences, Průhonice, Czech Republic; ⁴School of Natural Sciences, University of Galway, Galway, UK; ⁵Helmholtz Centre for Environmental Research – UFZ, Halle, Germany; ⁶Research Institute of Nature and Forest, Brussels, Belgium; ⁷Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany; ⁸Freie Universität Berlin, Berlin, Germany; ⁹Department of Evolutionary and Environmental Biology, University of Haifa, Haifa, Israel; ¹⁰CNRS, AgroParisTech, Ecologie Systématique Evolution, Université Paris-Saclay, Gif-sur-Yvette, France; ¹¹Biology Centre of the Czech Academy of Sciences, Institute of Hydrobiology, České Budějovice, Czech Republic; ¹²Department of Ecoscience, Aarhus University, Aarhus, Denmark; ¹³Department of Ecology, Faculty of Science, Charles University, Prague, Czech Republic; ¹⁴Faculty</footnote>
of Natural and Agricultural Sciences, Ovidius University of Constanta, Constanta, Romania; ¹⁵Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Stellenbosch, South Africa; ¹⁶Centre for Environmental and Marine Studies and Department of Biology, University of Aveiro, Aveiro, Portugal; ¹⁷CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Universidade do Porto, Porto, Portugal; ¹⁸BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Vairão, Portugal; ¹⁹NBI, Natural Business Intelligence, Tec Labs 1.2.1, Campus da Faculdade de Ciências da Universidade de Lisboa, Lisbon, Portugal and ²⁰Meise Botanic Garden, Brussels, Belgium

## ACKNOWLEDGEMENTS

This work originated from a workshop supported by the COST Action Increasing understanding of alien species through citizen science (COST Action CA17122). DMR acknowledges support from the Centre for Invasion Biology, Stellenbosch University, Mobility 2020 project no. CZ.02.2.69/0.0/0.0/18_053/0017850 (Ministry of Education, Youth and Sports of the Czech Republic) and long-term research development project RVO 67985939 (Czech Academy of Sciences). ASV acknowledges support from the FCT-Portuguese Foundation for Science and Technology through the program Stimulus for Scientific Employment-Individual Support (contract reference 2020.01175.CEECIND/CP1601/CT0009), and project ClimateMedia-Understanding climate change phenomena and impacts from digital technology and social media (contract reference 2022.06965.PTDC). IJ and PP acknowledge support from the Czech Science Foundation (project no. 23-07278S). HT acknowledges FCT/ MCTES support to CESAM (UIDP/50017/2020+UIDB/50017/20 20+LA/P/0094/2020). NP acknowledges the Animal Ecology Lab (Sascha Buchholz, Hilke Hollens-Kuhr) and the University of Münster for their support in running the workshop and Emily Brunner for co- producing the illustrations. We thank Tom August for helpful com- ments on the manuscript as well as Wouter Koch and an anonymous reviewer for their valuable advice on improving the article. Open Access funding enabled and organized by Projekt DEAL.

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

## DATA AVAILABILITY STATEMENT

This work does not contain any original data.

## ORCID

Nadja Pernat https://orcid.org/0000-0003-2244-1002

Susan Canavan https://orcid.org/0000-0002-7972-7928

Marina Golivets https://orcid.org/0000-0003-1278-2663

Jasmijn Hillaert https://orcid.org/0000-0002-3761-8846

Yuval Itescu https://orcid.org/0000-0002-6301-7112

Ivan Jarić https://orcid.org/0000-0002-2185-297X

Hjalte M. R. Mann https://orcid.org/0000-0002-4768-4767

Pavel Pipek https://orcid.org/0000-0003-1116-1013

Cristina Preda https://orcid.org/0000-0002-3513-7344

David M. Richardson https://orcid.org/0000-0001-9574-8297

Heliana Teixeira https://orcid.org/0000-0001-8525-9967

Ana Sofia Vaz https://orcid.org/0000-0001-6588-4650

Quentin Groom https://orcid.org/0000-0002-0596-5376

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**How to cite this article:** Pernat, N., Canavan, S., Golivets, M., Hillaert, J., Itescu, Y., Jarić, I., Mann, H. M. R., Pipek, P., Preda, C., Richardson, D. M., Teixeira, H., Vaz, A. S., & Groom, Q. (2024). Overcoming biodiversity blindness: Secondary data in primary citizen science observations. *Ecological Solutions and Evidence, 5*, e12295. https://doi.org/10.1002/2688-8319.12295