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# Using reflectance spectra and Pl@ntNet to identify herbarium specimens: a case study with *Lithocarpus*

Barbara M. Neto-Bradley¹ ID, Pierre Bonnet² ID, Hervé Goëau² ID, Alexis Joly³ ID,
Jeannine Cavender-Bares⁴,⁵ ID and David A. Coomes¹ ID

¹Department of Plant Sciences and Conservation Research Institute, University of Cambridge, Cambridge, CB23EA, UK; ²AMAP, University of Montpellier, IRD, CNRS, CIRAD, INRAE, Montpellier, 34980, France; ³Inria, LIRMM, University of Montpellier, CNRS, Montpellier, 34095, France; ⁴Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; ⁵Department of Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN 55108, USA

Authors for correspondence:
Barbara M. Neto-Bradley
Email: [barbaranetobradley@gmail.com](mailto:barbaranetobradley@gmail.com)

David A. Coomes
Email: [dac18@cam.ac.uk](mailto:dac18@cam.ac.uk)

Received: 9 November 2024

Accepted: 9 May 2025

New Phytologist (2025)

doi: 10.1111/nph.70258

**Key words:** herbarium specimens, indeterminate specimens, leaf reflectance spectra, *Lithocarpus*, Pl@ntNet, species identification.

## Summary

* The digitisation of plant collections is bringing large quantities of information into accessible electronic databases. However, in recent decades, traditional taxonomic work in collections has declined, meaning that more specimens are only determined to family or genus, particularly when lacking key identification structures. If unaddressed, large-scale digitisation risks widening the gap between well-studied species and those lacking data.

* Hyperspectral reflectance and computer vision are two emerging approaches for identifying species, but these have yet to be cross-compared for herbarium-based taxonomy. Using *Lithocarpus* species as a case study, we compared classification accuracy obtained from leaf reflectance spectra with computer vision (implemented via Pl@ntNet), a RGB (red, green, blue) image-based approach known to work well on specimens presenting reproductive structures. In the spectral approach, we assessed how much data are needed to optimise classification accuracy, how many species could be discriminated between, and whether close relatives were more frequently confounded.

* We found that *Lithocarpus* herbarium specimens were accurately identified to species from relatively small spectral datasets. Despite not incorporating reproductive structures, this was only 14% less accurate than Pl@ntNet.

* We suggest these rapid, nondestructive leaf reflectance measurements, paired with computer vision, could fill identification gaps in collections, particularly for specimens lacking reproductive features.

## Introduction

Carefully curated natural history collections provide a wealth of biodiversity data, which can help us understand how species are responding to change. Long-term, repeat observations facilitate the identification of changes in species' morphology, phenology and ranges across space and time (Knapp et al., 2012). Yet for many regions and species, particularly in the global south, we lack spatially and temporally stratified datasets (Yesson et al., 2007; Gonzalez et al., 2016; Meyer et al., 2016). In such cases, herbarium specimens from natural history collections provide an opportunity to make up for this missing biodiversity data gap faster than new fieldwork (Vargas et al., 2024), although we note that species from the global south are also less likely to have repeat exemplars through time. Herbarium specimens provide a snapshot of biodiversity at a particular time and place, and are increasingly recognised for their value to research. Large digitisation projects of natural history collections can enhance accessibility for researchers to leverage these specimens in addressing various

ecological and evolutionary knowledge gaps. However, as most research and conservation work are organised around species-level data, the scientific community risks overlooking a substantial portion of biodiversity in those specimens, which remain unidentified (i.e. 'indeterminate') in collections, despite digitisation (Lavoie, 2013; Daru et al., 2018; Marsico et al., 2020). Indeterminate specimens, those which have only been identified to the family or genus level, are common in collections (as illustrated in Fig. 1). They are often kept in collections because over the course of a specimen's lifetime, they might eventually become identified if experts exchange material or annotate specimens outside their home institution.

The limitations of herbarium data must be carefully considered to maximise the utility of large-scale digitisation projects (Bebber et al., 2010; Meineke et al., 2018; Heberling & Mason, 2022). One fundamentally challenging aspect of using herbaria to fill knowledge gaps is that restricted curatorial resources for historically understudied species have likely already resulted in missing metadata. Limited curation, perpetuated by a
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Depiction of identified herbarium specimens for species in the genus *Lithocarpus*, which were collected between 1850 and 2020. Records (downloaded from GBIF on 28 October 2024) are binned into 5-yr intervals. The records collected in each time interval are shaded in purple, in proportion to how many have only been identified to genus vs species, shown on the left y-axis. Portions of each bar shaded in dark purple represent specimens which have only been identified to genus (i.e. 'Lithocarpus indet.') and portions shaded light purple represent those identified to species. The overlaid line graph in white illustrates the number of *Lithocarpus* specimens collected in each time interval, shown on the right y-axis.</figcaption></figure>

decline in taxonomists and taxonomic work, can lead to the separation of plant material from records of when and where something was collected and from taxonomic analyses of the specimen, exacerbating identification gaps (Wheeler, 2014; Bik, 2017). This gap is compounded by the numerous specimens that lack reproductive structures. For many species, variation in reproductive structures provides the key morphological distinctions needed for species identification. In species that do not flower or fruit frequently, these features can easily elude collectors. For example, in remote and understudied regions, vouchers have often been collected during time-limited forays or expeditions, where it was difficult to ensure that all species were fertile during the collection period, resulting in a significant fraction of specimens which only contain vegetative structures (Park et al., 2023). Large-scale digitisation projects can risk entrenching the existing geographic and taxonomic biases in research by providing additional digital datasets for species that are already identified, well curated and better studied (Eichhorn et al., 2020; Feng et al., 2022). Nonetheless, if designed to do so, these digitisation projects also have the potential to address and correct these biases (Drew et al., 2017).

Leaf reflectance spectra offer a complementary, nondestructive measurement for inclusion in herbarium digitisation projects, which could mitigate missing metadata. Leaf reflectance spectra are a measure of how light interacts with an object, and as such they encode the object's chemical and physical properties (Fig. 2). These nondestructive *in situ* or remotely sensed biodiversity measurements have been used to identify plant species and predict their traits from leaf spectra (Cavender-Bares et al., 2016) or aerial imagery (Martin et al., 1998; Williams et al., 2021; Sapes et al., 2024); ongoing work highlights their potential for use with herbarium specimens (Bazan, 2024; Santa cruz Endara, 2024). Kothari et al. (2023) showed that species identity and leaf traits are accurately estimated from spectra across fresh, dry and ground leaf tissue even when the dry material has been stored and gained discoloration. The consistent

interspecific variation in leaf reflectance spectra has even been leveraged to distinguish species from their hybrid offspring with high accuracy (Stasinski et al., 2021). The effectiveness of spectroscopic analyses of fresh and recently dried leaves is well established (Burnett et al., 2021), but herbarium specimens differ from freshly dried leaves in several ways, and the efficacy of extending these approaches to include them has been underexplored (Durgante et al., 2013). Herbarium specimens are usually glued, taped or sewn onto sheets of paper from which they cannot be removed; they vary substantially in age (e.g. some are > 100 years old), they are collected out of convenience (e.g. from low branches and from the side of the road), and are sometimes stored for many days after collection before they are pressed (i.e. with unknown drying speeds and conditions). Herbarium specimens are limited in number and will provide relatively small training datasets for machine learning models. All these differences could make it hard to achieve reliable species identifications using spectral approaches. Consequently, evaluating the accuracy and efficacy of novel digitisation approaches for herbaria is critical before designing further work. Despite these challenges, the nondestructive nature of spectral measurements is a key advantage to be leveraged in light of the ongoing need to minimise destructive sampling of herbarium specimens and preserve them for future reference (Davis et al., 2024).

Machine learning approaches for identification, which leverage RGB images, are another nondestructive method that can be used to fill collection metadata gaps. The web- and mobile-based platform Pl@ntNet uses advanced machine learning techniques to identify RGB images of plants with high accuracy (Joly et al., 2016). It was originally designed for *in situ* observations to support plant management and conservation efforts (Bonnet et al., 2020), but the platform has since been widely adopted by the scientific community and citizen scientists for identifying plant species through pictures of their organs (particularly, flowers, fruits and leaves), including from herbarium specimens. Its core technology is based on computer vision models, specifically
<figure><img src="image_4.png" /><figcaption>**Fig. 2** Depiction of our herbarium sampling approach and how this influences reflectance measurements. We highlight the difference between reflectance measurements from a leaf placed on a herbarium sheet (line in medium purple) vs the same leaf measured with no background (line in dark purple) in contrast to the reflectance spectra of the page itself (line in light purple). For all our analyses, we use spectra of leaf tissue measured on herbarium sheets. In our sampling design, we derived training and validation datasets for both an RGB image-based classifier, implemented through PI@ntNet and a spectral-trained linear discriminant analysis (LDA) to identify species of *Lithocarpus*, using the same suite of herbarium specimens. The spectral data encode information about the chemical and physical properties of a specimen's leaves, whereas the RGB images also contain information about leaf shape and reproductive structures, when these are present.</figcaption></figure>

leveraging deep neural networks to process vast amounts of cross-validated plant data. At the time of writing, this includes 13 852 100 million plant images across 51 911 species. While combining and contrasting RGB classification to spectral classification is common in remote sensing, to our knowledge, this has not yet been performed in the context of identifying specimens in herbarium collections.

We focused on a subset of species in the genus *Lithocarpus*, as a case study for exploring the added value of measuring spectra on herbarium specimens, the sampling approach needed, and how this performs compared with RGB image-based classification methods. We used this lineage as a case study and posit that it meets the criteria for relevant application of these methods. First, species in the genus are difficult to identify in the absence of key reproductive structures, such as fruits, but specimens are frequently collected in vegetative form (Camus, 1952; Soepadmo

& van Steenis, 1972). Second, there are over 320 described species of *Lithocarpus* that span from India, East and Southward through New Guinea, and some hotspots of diversity (e.g. Mount Kinabalu) host up to 40 different species of *Lithocarpus* on the same mountain (Cannon, 2001). This high degree of sympatric species diversity underpins the need for species classification methods that do not rely on seasonal reproductive structures. To work at a scale that reflects this need, we limited our geographic focus to Malaysia, Brunei and Indonesia. We photographed and measured leaf reflectance spectra for 1467 identified specimens of *Lithocarpus* from this region, which were housed in the Herbarium at Kew (K). We then used this dataset to evaluate a sampling approach for spectra by addressing three key questions: How many measurements are needed to train accurate predictive models? How many species can be reliably distinguished in a single analysis? And does similarity in leaf traits
influence identification accuracy? We then carried this forward to compare our spectral approach with an existing RGB image-based classification method implemented through PI@ntNet.

Materials and Methods

Taxonomic and geographic scope

We focused on specimens of stone oaks in the genus *Lithocarpus*
(Blume) from the collections of the Royal Botanical Gardens,
Kew (K) Herbarium (Richmond, UK). Specimens were selected
from Kew's 6C region, which covers Brunei, Indonesia and
Malaysia, with the majority of specimens originating from the
island of Borneo. We prioritised looking at species that were best
represented in this region. In total, we measured 1467 specimens
across 31 species (specimen accessions are provided in Supporting
Information Dataset S2, see later). The oldest record we observed
was collected in 1850, and the youngest was collected in 2014.

Data collection

**Spectral data** In February 2022, we collected spectral reflectance measurements of herbarium specimens using a SVC HR-1024i Field Spectrometer (Spectra Vista Corp., Poughkeepsie, NY, USA) equipped with a leaf clip attachment. During reflectance measurements, the gasket on the leaf clip was removed so that the spectrometer's lens could be pressed flat against the leaf tissue being measured. The spectrometer was provided and maintained by the UK's Natural Environment Research Council's Field Spectroscopy Facility (FSF, Edinburgh, UK). We measured reflectance of the adaxial surface of the leaves, avoiding major veins and visibly discoloured or damaged sections when possible. For each specimen in our dataset, we measured the reflectance of a preserved leaf glued to a herbarium sheet (as illustrated in Fig. 2). All measurements were taken on a single white-surfaced desk, in a dark room with lights turned off and blinds drawn. Although not explored in these analyses, when more than one usable leaf was available per specimen, we measured up to three unique leaves, and for every specimen, we also measured the reflectance of the herbarium paper itself (Table S1). We calibrated measurements against a white Spectralon panel (Labsphere, North Sutton, NH, USA) every two to six measurements, between each specimen. For all spectral analyses, we use a single reflectance measurement from one leaf to represent each unique specimen. The reflectance spectra were filtered to remove readings with reflectance values > 1. We trimmed the measurements to omit noisy regions, and after cleaning, only include wavelengths between 400 and 2400. We matched the data around the sensors, using the R package SPECTROLAB (Meireles & Schweiger, 2021). After cleaning, we smoothed, normalised and standardised the reflectance spectra before running the analyses outlined below.

**Specimen image & metadata** We photographed each specimen using a camera mounted above a well-lit flatbed. Specimens were

placed on the flatbed alongside a ruler and colour checker card.
We saved these as RGB images. For each specimen, we recorded
the accession number, Latin name and collection locality. All spe-
cimens used had previously been identified, with many including
determinations or confirmed identifications by Engrik Soe-
padmo, a taxonomist whose expertise included Lithocarpus (Soe-
padmo & van Steenis, 1972; Soepadmo et al., 2000).

Analyses of spectral data

To assess the feasibility of using spectra to identify indeterminate herbarium species, we aimed to understand the effect of sampling on predictive power. Herbarium specimen data represent a form of convenience sampling, meaning that specimens are not always available in abundance for training identification models. As such, optimising the number of training samples relative to model accuracy was important for examining this method's transferability to smaller natural history collections. We first focused on three aspects of sampling: (1) how does model accuracy change with more training observations per species? given this; (2) how does the number of species included impact the model accuracy?; and (3) can this approach successfully discriminate between closely related species? Throughout, we used linear discriminant analysis (LDA) to predict species identity from the leaf reflectance spectra, with 80% of the data used for training and 20% for validation. Linear discriminant analysis is an established method used for spectral classification of plant species, from remote sensing to point measurements (Durgante et al., 2013; Guzmán et al., 2020; Ball et al., 2024). Partial least squares discriminant analysis is another common method for analysing spectral data; however, in preliminary tests, we found LDA to provide higher classification accuracy for these species with smaller training datasets (Fig. S1). We built these models using the R packages CARET and MASS (Kuhn, 2008; Venables & Ripley, 2010). We focused on reflectance measurements taken from the leaves mounted on the herbarium page (excluding loose leaves in packets) so that all records would be immediately comparable.

**Effect of number of specimens per species on classification accuracy** To determine how many records are needed per species to reliably predict species identity, we focused on the three best-represented species in our dataset (*Lithocarpus nieuwenhuisii* (Seemen) *A. Camus*, *Lithocarpus leptogyne* (Korth.) Soepadmo and *Lithocarpus gracilis* (Korth.) Soepadmo; Table S1). This allowed us to explore how prediction accuracy changes over a 10-fold increase in available records, ranging from 10 to 100 records per species. All records used had been previously identified to species, enabling straightforward evaluation of prediction accuracy. We built classification models using an increasing number of observations, starting with 10 records per species and incrementing by 10 up to a maximum of 100 records. For each sample size (10, 20, 30 ... 100), we repeated the model-building process 100 times to account for uncertainty due to sampling. To quantify the relationship between the number of observations and model accuracy, we fit a segmented linear regression using the R package SEGMENTED (Muggeo & Vm, 2008). By identifying
a breakpoint, we determined the least number of specimens for
which we optimise improvements in classification accuracy.

**Effect of number of species on classification accuracy** A second focus for developing an effective sampling strategy involved understanding how predictive power changed as more species were added to the dataset. To explore this, we used all species for which we had at least 30 specimens, based on our previous analysis. After filtering, the dataset contained 17 species. Using LDA as above, we evaluated the impact of increasing the number of species in the dataset on model accuracy. We built classification models using an increasing number of species, ranging from 2 to 17, and repeated this with all unique combinations of species at every sampling level. This ensured that our results were not biased by particular species combinations that are unusually easy or difficult to differentiate.

**Effect of leaf trait similarity on classification accuracy** To assess our ability to discriminate between species even when they might share high overlap in described leaf traits, we examined the relationship between misclassification rate for each species pair in the dataset relative to their overlap in 53 leaf traits. We estimated the overlap in species' leaf traits by converting leaf descriptions from Tree Flora of Soepadmo and Saw (2000) into binary traits (e.g. elliptic leaf shape: yes/no) or numerical ranges (e.g. leaf length: 6–15 cm). For each trait, we categorised whether combinations of species were 'distinct' or 'similar'. For binary traits, species pairs with a combination of 0 and 1 values were considered distinct. For continuous traits, pairs of species which had nonoverlapping ranges for the trait of interest were considered distinct. For each species pair, we then counted how many of the 53 traits were similar vs distinct, to generate an overall leaf trait similarity score. Using this approach, we populated a matrix of similarity scores for the 17 species. To assess misclassification rate, we repeated the model-building exercise described in the *Effect of number of species on classification accuracy* section 100 times to incorporate variation from specimens sampled for training vs validation data. We calculated the misclassification rate as the proportion of specimens of Species A that were incorrectly identified as Species B. We then fit a linear regression, to assess whether overall leaf trait similarity was a good predictor of these misclassification rates. Finally, to identify whether specific trait differences between species were more likely to be associated with spectral classification error, we ran a redundancy analysis (RDA) with pairwise misclassification rates explained by the full matrix of pairwise trait differences between species. We also ran a similar analysis, based on phylogenetic proximity, presented in *Notes* S1.

**RGB image-based classification methods** Lastly, to contextualise the utility of approaches using reflectance spectra for classification tasks, we compared the accuracy of our spectral identification approach with that of a RGB image-based classifier, implemented through Pl@ntNet. The model is primarily built upon the vision transformer (ViT)-based DINOv2 architecture (Oquab *et al.*, 2023), which provides robust visual embeddings

for plant identification tasks (Goëau *et al.*, 2023). Both models (Pl@ntNet's image-based classifier and the spectral-based LDA) were trained and validated using data from the same set of herbarium specimens (30 specimens per species across 17 species, split into 80% training data, 20% validation data). For the purpose of this work, Pl@ntNet has been fine-tuned to specialise in identifying plant species from herbarium specimen images, a unique use case that differs significantly from the model's original application on fresh or living plant images. Herbarium specimens, typically consisting of pressed and dried plant material, provide valuable morphological information but present distinct challenges for machine learning due to differences in texture, colour and the presence of handwritten or printed labels (Goëau *et al.*, 2021). To address this, the classification head of the Pl@ntNet model was fine-tuned specifically for herbarium specimens while keeping the pretrained DINOv2 embeddings.

For fine-tuning, the classification head was retrained using 24 herbarium images across 17 species. This approach preserved the strong generalist knowledge captured by the original DINOv2 embeddings, allowing the extraction of useful visual features from plant images, while tailoring the classification layers to the unique properties of herbarium specimens. By keeping the original embeddings, the fine-tuning process benefited from the generalist nature of the DINOv2 model, which has been trained on a wide diversity of plant images, ensuring that the model retains its capability to recognise a broad array of features even in 'less-than-ideal' imaging conditions typical of herbarium collections.

## Results

We found that with greater numbers of specimens used for training and validating three species' discrimination, prediction accuracy for species identity increases, as does precision (Fig. 3). The mean prediction accuracy when using only 10 records per species is 0.72 (SD = 0.18) vs 0.92 (SD = 0.04) when this dataset is tenfold (Fig. 3). Over this same range, the kappa coefficient is 0.58 (SD = 0.26) and 0.89 (SD = 0.06) (Fig. 3). To identify the least number of specimens for which we would have the greatest accuracy, we fit a segmented linear regression to assess the relationship between number of specimens and accuracy and identify a breakpoint. The overall model was significant, explaining 35% of the variance in accuracy ($R^2$ = 0.35, *F*(1, 997) = 352.95, *P* < 0.0001). We found a breakpoint at 32 (95% CI (29, 35)) specimens. Before the breakpoint, the number of specimens had a positive relationship with accuracy (slope = 0.0076, 95% CI (0.0064, 0.0087)). After the breakpoint, this relationship remained positive but became less steep (slope = 0.00046, 95% CI (0.00016, 0.00077)). These results indicate that past 32 specimens per taxa, the added value for each specimen to our model performance decreases.

We then focused on all 17 species for which we had at least 30 unique specimens and evaluated the relationship between the number of species being discriminated between and model performance. As the number of species in the dataset increases, the prediction accuracy for species identity declines. The mean prediction accuracy when only discriminating two species is 0.89
<figure><img src="image_5.png" /><figcaption>**Fig. 3** Depiction of the linear discriminant analysis (LDA) model's accuracy (a) and kappa (b), for discriminating between three species (*Lithocarpus nieuwenhuisii*, *gracilis* and *leptogyne*), across an increasing availability of specimens spectral reflectance data for model construction. At each level, 80% of the data were used to train an LDA and the remaining 20% were used for validation. An asterisk represents the mean accuracy at each level, and the error bars represent one SD from this mean.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 4** Depiction of the linear discriminant analysis (LDA)'s accuracy (a) and kappa (b) for discriminating between an increasing number of species, using 30 specimens reflectance spectra per species. To help capture the variability in model performance, for each number of taxa, all possible combinations within the full set of 17 species were considered. In each iteration, 80% of the data were used to train the model, and the remaining 20% were used to validate it. An asterisk represents the mean accuracy at each level, and the error bars represent one SD from this mean.</figcaption></figure>

(SD = 0.11) vs 0.65 (SD = 0.06) when discriminating between 16 species (Fig. 4). Over this same range, the kappa coefficient is 0.77 (SD = 0.22) and 0.63 (SD = 0.05) (Fig. 4).

For these 17 species, we fit a linear regression to evaluate the relationship between misclassification rates from our spectral-LDA and overall similarity in leaf traits. The relationship was not significant, *F*(1, 270) = 0.8551, *P* = 0.34, with the

model explaining no variance in misclassification rates (Adj R² = 0.00, Fig. 5a). However, a RDA revealed a significant association between trait differences and misclassification, *F*(44, 227) = 4.07, *P* = 0.001, explaining 34% of the variance (Adj R² = 0.34; Fig. 5b). These results suggest that spectral misclassification rates are higher when species differ primarily in traits related to leaf shape and size.
<figure><img src="image_7.png" /><figcaption>**Fig. 5** Description of the spectral misclassification between 17 species in the dataset in relation to their described leaf trait similarity. (a) Error rates between each species pair in the dataset (i.e. how frequently Species A is misclassified as Species B by the spectral model), which are plotted against the overlap in described leaf traits between each species pair. To show the full spread of variation in these error rates, every combination of taxa in the dataset is shown. (b) Variance in spectral misclassification rates, explained by differences in leaf trait categories, based on a redundancy analysis (RDA).</figcaption></figure>

Comparing across a validation dataset of 102 specimens, the spectral-trained LDA accurately predicted species ID for 66% of specimens, while the image-trained model (Pl@ntNet) was 80% accurate (Fig. 6). Pl@ntNet generally had higher accuracy; however, both models made correct predictions for specimens which the other model missed (Fig. 7). As an example, Pl@ntNet was 100% accurate at identifying specimens of *Lithocarpus echinifer* where the LDA was only 33% accurate (Fig. 6). In turn, the LDA was 83% accurate at identifying specimens of *Lithocarpus urceolaris*, where Pl@ntNet was only 67% accurate (Fig. 6). Predictions for 61 specimens of 102 were in agreement across both models and matched the ground truth (Fig. 7). For 14 specimens, neither model's predictions matched the ground truth, but of these, four predictions were in agreement between both models (Fig. 7). These results suggest that both methods can bring added

value to automated species classification, with key opportunities to leverage spectral classification in the absence of reproductive structures.

## Discussion

Using a realistic number of observations for the scale of a single collection, we found that rapid, nondestructive reflectance spectra measurements from herbarium specimens can be leveraged to accurately identify closely related species. Our results also demonstrate that the performance of spectral-based classification was close to the accuracy of image-based methods, even though the latter benefits from information provided by reproductive structures. We highlight the potential for using these approaches to identify 'indeterminate' specimens in collections, with spectra proving particularly valuable for specimens that lack reproductive structures.

## Relevance of *Lithocarpus* as a case study

In our study, we focused on species in the genus *Lithocarpus*, sampling those that co-occur in Malaysia, Indonesia and Brunei, primarily on the island of Borneo. This region, known for its high biodiversity and endemism, presents a particularly challenging case for identifying species of *Lithocarpus* from herbarium specimens for multiple reasons. In particular, herbarium records show historical discrepancies in place names; many specimens were collected without fruits, despite these being key features for identification, and many specimens in collections have not been observed by an expert since before the most recent taxonomic revision (Soepadmo & Saw, 2000). These challenges make the *Lithocarpus* from Borneo an especially relevant case for evaluating our methods, as similar conditions likely apply to many understudied lineages and regions. We emphasise that this case study should be viewed as a particularly challenging scenario. We expect that prediction accuracy will improve as this field develops, and modelling approaches are designed to be particularly sensitive to the herbarium use case. We also anticipate that the accuracy achieved here will be easily surpassed when applied to more clearly defined taxonomic groups, when leveraging recently collected specimens, or when incorporating more metadata, such as spatial features.

## Sampling approaches designed for herbarium collections

While accurate species predictions depend on a sufficient number of observations regardless of the method used, collections will always have limited numbers of specimens. In this study, we justified the number of training samples needed to optimise classification accuracy by using a subset of our data to define the relationship between these, including a breakpoint analysis. To create a realistic case study for smaller herbaria, we aimed to use the fewest observations while optimising for accuracy. In our analysis, this involved 30 specimens per species, although we highlighted that this number will vary depending on the taxonomic groups involved and the ease of distinguishing their subunits. We
<figure><img src="image_8.png" /><figcaption>**Fig. 6** Confusion matrices for two methods of classifying 17 species of *Lithocarpus*, using the same set of herbarium specimens to train and validate both models. (a) Confusion matrix for a linear discriminant analysis (LDA) trained on spectral reflectance data from the leaves of herbarium specimens. (b) Confusion matrix for a vision transformer (ViT), implemented through Pl@ntNet, trained on whole images of each herbarium specimen. Numbers on the diagonal indicate correctly identified species; misclassifications are shown on the off-diagonals. Darkest shades of purple highlight higher proportions of validation specimen predictions, while lighter shades illustrate smaller proportions.</figcaption></figure>

suggest that characterising this relationship and identifying the
optimal sampling strategy should be independently established
for each use case of these methods. As increasing the number of
observations improves accuracy, we present our results as a lower
bound of what can be achieved rather than a best-case scenario.

In training our model with equal numbers of observations for
each species, we avoided embedding assumptions about species'
frequency in collections. With additional species-level informa-
tion, models could be trained in proportion to real-world or
collection-level abundances. This would enable a greater propor-
tion of specimens to be incorporated, leading to an overall greater
number of observations available to include in training datasets.
Further improvements in accuracy could be achieved by taking
multiple spectral scans per specimen and scanning both adaxial
and abaxial sides of leaves as shown in Durgante et al. (2013).
However, as we found, this may not always be feasible depending
on the condition of the material and how it is mounted on her-
barium sheets. Although we gathered scans of loose leaves and
the herbarium paper on its own, we did not make use of these
data in these analyses. Instead we focused on scans from leaves
glued to herbarium sheets, for which we had more data. We
believe this is suitable for the within-collection questions
we address here, but note this may not be appropriate for aggre-
gation with scans collected from other collections. As such, addi-
tional opportunities for improving classification accuracy might
exist by 'removing' the effect of the herbarium paper's spectral

signature from that of the leaf, or only using loose or un-glued
leaf tissue if this is possible.

Spectral classification distinguishes between closely related species which co-occur

Clear resolution of closely related species through spectral classifi-
cation broadens the utility of this method, especially for geo-
graphic regions with high congeneric overlap or lineages that
have experienced recent speciation events. Our results demon-
strate that the spectral classification method does not confuse clo-
sely related species at a higher rate than more distantly related
ones (Fig. S2). Our findings contribute to a growing body of
infra-generic studies (Durgante et al., 2013; Cavender-Bares
et al., 2016; Deacon et al., 2017; Stasinski et al., 2021; Santacruz
Endara, 2024), which underscore the capacity to confidently
resolve closely related species using spectral reflectance. In natural
history collections from highly biodiverse regions, this fine-scale
taxonomic resolution will likely prove useful in making up for
lacking metadata, particularly when additional data on distin-
guishing features, such as geographic or temporal information are
missing.

As in most large tropical lineages, there is a possibility that the
taxonomy of *Lithocarpus* requires revision; for example, unde-
scribed species may potentially be housed in herbaria under other
existing species names. We expect such revisions to improve
<figure><img src="image_9.png" /><figcaption>**Fig. 7** Venn diagram illustrating the overlap between species identity predictions derived from the spectral-trained linear discriminant analysis (LDA) and an image-trained classifier (implemented via PI@ntNet). Both models used training (n = 408) and validation (n = 102) datasets derived from the same set of herbarium specimens. Regions of overlap with the ground truth (green circle), indicate the number of model predictions from the validation dataset, which matched the herbarium label. Regions of overlap between the spectral prediction and PI@ntNet prediction (both purple circles) highlight validation specimens for which both models agreed in their predictions. The diagram shows that 61 individuals (out of 102) were accurately classified by both approaches, 21 species were predicted only by PI@ntNet, six species predicted only by spectra, and 14 species were not accurately classified by either approach.</figcaption></figure>

classification accuracy by reducing noise in the training data rather than worsening it (Durgante, 2024). We elaborate on the opportunities to leverage spectral data in taxonomic revisions below.

## Reflectance spectra amid a broader landscape of automated classification

We show that both reflectance spectra and RGB image-based methods offer significant value in automated species classification (Figs 6, 7). For *Lithocarpus*, as with many other species, the key distinguishing features lie in the morphology of mature fruits (Camus, 1952; Soepadmo & van Steenis, 1972). Consequently, image-based methods that incorporate these structures will be particularly useful for classification. Despite this, the spectral method performs surprisingly, with only a 14% reduction in accuracy compared with the image-based approach, even though it does not make use of reproductive information. This highlights the potential to confidently identify indeterminate specimens in collections that lack fruit or flowers using spectra. Our results illustrate that many species with similar leaf traits can be accurately distinguished with spectral classification (Fig. 5a). As expected, we find that classification errors are more likely when species differ primarily in leaf shape or size, features not directly captured by spectra, as opposed to texture or venation (Fig. 5b). It may be possible to mitigate some of these shortcomings of spectral-only approaches by integrating with image-based methods, such as PI@ntNet.

We highlight two promising future directions that leverage both spectral and RGB image methods. First, considering the predictions of both models separately offers a way to flag

potentially misidentified and mislabelled specimens with confidence, for example, where both models agree in their predictions but disagree with the herbarium label. Second, as both methods accurately predict species that the other model may miss, joint approaches that use spectral and image data together may provide a way to boost automated classification accuracy in challenging lineages. As a preliminary test, we implemented a simple approach that combines predictions from both models, weighting each based on the likelihood of accurate identification for the predicted species (Notes S2; Fig. S3). This approach is further detailed in the Supporting Information. We note several other ways of combining these approaches that should be explored.

## Opportunities from including reflectance spectra as part of the extended specimen

The importance of retrieving as much information as possible from historical specimens is growing. Natural history collections provide an efficient way to gather data on understudied taxa, compared with new fieldwork (Vargas et al., 2024). This can be leveraged to both understand and forecast plants' responses to climate change and other anthropogenic pressures (Feeley & Silman, 2010; Panchen et al., 2012; Meineke et al., 2019; Neto-Bradley et al., 2021). Nonetheless, an estimated 30–37% of vascular plant species are represented by fewer than five observations in herbaria and ecological collections (Enquist et al., 2019), with small collections contributing key elements for biodiversity coverage (Marsico et al., 2020). This makes it crucial that we maximise the utility of existing data, particularly in regions and lineages for which it is scarce. Through our case study of the genus *Lithocarpus*, we explore how reflectance spectra can help address knowledge gaps and identify indeterminate records in collections as part of digitisation efforts. We demonstrate the effectiveness of this method even at the scale of a single collection, while noting some areas for improvement, which include refining sampling strategies and coupling spectra with image-based classification. We hope this tool will be used to bridge the knowledge gaps for understudied species and regions as mentioned in this paper, but we also highlight that there are many exciting opportunities open for leveraging spectral data from specimens more broadly. While estimating plant traits, such as leaf chemistry, is not a new application of field-collected spectral reflectance data (Kokaly et al., 2009; Serbin et al., 2016; Schneider et al., 2017), its transferability to natural history collections has been in development (Kothari et al., 2023). Emerging work now affirms a suite of these traits which can accurately be estimated from historical herbarium specimens using non-destructive spectral measurements (Kühn et al., 2024; A. K. Lee et al., unpublished; White et al., 2025). These measurements will extend what we can learn from the biodiversity time series housed in collections. In summary, we emphasise the value of spectra as part of the extended specimen, which will offer new possibilities to address questions and knowledge gaps as the field develops.
Acknowledgements

We thank the Natural Environment Research Council Field Spectroscopy Facility for the loan of a field spectrometer, and A Grey, J Gillepsie and R Ramsay for their assistance with this equipment. We thank A Moore, M Weech and T Utteridge for facilitating work at Kew. We thank A Guzman, D Meireles, D White and J Ball for discussions on using reflectance spectra in herbarium collections. We would also like to acknowledge the individuals, although too numerous to list, who have contributed to the collection, identification and curation of the herbarium specimens used in this study. We thank the ASCEND Biology Integration Institute (NSF DBI: 2021898) for insights and feedback.

Competing interests

None declared.

Author contributions

BMNB, JCB and DAC designed the study. BMNB collected the data. BMNB, PB, HG and AJ analysed the data. BMNB wrote the original draft. All authors contributed to the editing.

ORCID

Pierre Bonnet ID https://orcid.org/0000-0002-2828-4389

Jeannine Cavender-Bares ID https://orcid.org/0000-0003-3375-9630

David A. Coomes ID https://orcid.org/0000-0002-8261-2582

Hervé Goëau ID https://orcid.org/0000-0003-3296-3795

Alexis Joly ID https://orcid.org/0000-0002-2161-9940

Barbara M. Neto-Bradley ID https://orcid.org/0000-0002-6061-208X

Data availability

All original spectral data are available in the Supporting Information, in the file named 'Dataset S1'. The accession list of specimens observed is also available in the Supporting Information, in the file named 'Dataset S2'. The code used for cleaning and analysing this dataset is available at: https://github.com/bnetobradley/lithocarpus_spectra. A public interface to Pl@ntNet is accessible at: https://identify.plantnet.org/.

References

Ball JGC, Jaffer S, Laybros A, Prieur C, Jackson T, Madhavapeddy A, Barbier N, Vincent G, Coomes DA. 2024. Harnessing temporal and spectral dimensionality to map and identify species of individual trees in diverse tropical forests. *bioRxiv*. doi: 10.1101/2024.06.24.600405.

Bazan S. 2024. NIRS as an innovative tool for valorizing herbarium specimens: ongoing research at CIRAD. [Conference presentation] *International Botanical Congress*, 2024, Madrid.

Bebber DP, Carine MA, Wood JRI, Wortley AH, Harris DJ, Prance GT, Davidse G, Paige J, Pennington TD, Robson NKB et al. 2010. Herbaria are a

major frontier for species discovery. *Proceedings of the National Academy of Sciences, USA* 107: 22169-22171.

Bik HM. 2017. Let's rise up to unite taxonomy and technology. *PLoS Biology* 15: e2002231.

Bonnet P, Joly A, Faton J-M, Brown S, Kimiti D, Deneu B, Servajean M, Affouard A, Lombardo J-C, Mary L et al. 2020. How citizen scientists contribute to monitor protected areas thanks to automatic plant identification tools. *Ecological Solutions and Evidence* 1: 10987.

Burnett AC, Anderson J, Davidson KJ, Ely KS, Lamour J, Li Q, Morrison BD, Yang D, Rogers A, Serbin SP. 2021. A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression. *Journal of Experimental Botany* 72: 6175-6189.

Camus A. 1952. *Les chènes: monographie des genre Quercus*. Texte: monographie du genre *Lithocarpus* et addenda aux tomes 1, 2, 3. P. Lechevalier.

Cannon C. 2001. Morphological and molecular diversity in *Lithocarpus* (Fagaceae) of Mount Kinabalu. *Sabah Parks Nature Journal* 4: 45-69.

Cavender-Bares J, Meireles JE, Couture JJ, Kaproth MA, Kingdon CC, Singh A, Serbin SP, Center A, Zuniga E, Pilz G et al. 2016. Associations of leaf spectra with genetic and phylogenetic variation in oaks: prospects for remote detection of biodiversity. *Remote Sensing* 8: 221.

Daru BH, Park DS, Primack RB, Willis CG, Barrington DS, Whitfeld TJS, Seidler TG, Sweeney PW, Foster DR, Ellison AM et al. 2018. Widespread sampling biases in herbaria revealed from large-scale digitization. *New Phytologist* 217: 939-955.

Davis CC, Sessa E, Paton A, Antonelli A, Teisher JK. 2024. Guidelines for the effective and ethical sampling of herbaria. *Nature Ecology & Evolution* 2: 1-8.

Deacon NJ, Grossman JJ, Schweiger AK, Armour I, Cavender-Bares J. 2017. Genetic, morphological, and spectral characterization of relictual Niobrara River hybrid aspens (*Populus × smithii*). *American Journal of Botany* 104: 1878-1890.

Drew JA, Moreau CS, Stiassny MLJ. 2017. Digitization of museum collections holds the potential to enhance researcher diversity. *Nature Ecology & Evolution* 1: 1789-1790.

Durgante FM. 2024. Identifying species using spectra of the bark in standing trees: testing the Micronir for recognizing species in the Amazon field. *Science Reporter* 12: 4772.

Durgante FM, Higuchi N, Almeida A, Vicentini A. 2013. Species spectral signature: discriminating closely related plant species in the Amazon with near-infrared leaf-spectroscopy. *Forest Ecology and Management* 291: 240-248.

Eichhorn MP, Baker K, Griffiths M. 2020. Steps towards decolonising biogeography. *Frontiers of Biogeography* 12: 4589.

Enquist BJ, Feng X, Boyle B, Maitner B, Newman EA, Jørgensen PM, Roehrdanz PR, Thiers BM, Burger JR, Corlett RT et al. 2019. The commonness of rarity: global and future distribution of rarity across land plants. *Science Advances* 5: eaaz0414.

Feeley KJ, Silman MR. 2010. Modelling the responses of Andean and Amazonian plant species to climate change: the effects of georeferencing errors and the importance of data filtering. *Journal of Biogeography* 37: 733-740.

Feng X, Enquist BJ, Park DS, Boyle B, Breshears DD, Gallagher RV, Lien A, Newman EA, Burger JR, Maitner BS et al. 2022. A review of the heterogeneous landscape of biodiversity databases: opportunities and challenges for a synthesized biodiversity knowledge base. *Global Ecology and Biogeography: A Journal of Macroecology* 31: 1242-1260.

GBIF.org. 2024. GBIF occurence download of *Lithocarpus* herbarium specimens. doi: 10.15468/dl.7a4m94.

Goëau H, Bonnet P, Joly A. 2021. Overview of PlantCLEF 2021: cross-domain plant identification. *Conference and Labs of the Evaluation Forum* 20: 1422-1436.

Goëau H, Bonnet P, Joly A. 2023. Overview of PlantCLEF 2023: image-based plant identification at global scale. In: *CLEF 2023 - working notes of the 24th conference and labs of the evaluation forum*, 1972-1981. Oakland, CA, USA: CLEF.

Gonzalez A, Cardinale BJ, Allington GRH, Byrnes J, Arthur Endsley K, Brown DG, Hooper DU, Isbell F, O'Connor MI, Loreau M. 2016. Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. *Ecology* 97: 1949-1960.
Guzmán QJA, Laakso K, López-Rodríguez JC, Rivard B, Sánchez-Azofeifa GA. 2020. Using visible-near-infrared spectroscopy to classify lichens at a Neotropical Dry Forest. *Ecological Indicators* 111: 105999.

Heberling JM, Mason HJ. 2022. Herbaria as big data sources of plant traits. *International Journal of Plant Sciences* 183: 87–118.

Joly A, Bonnet P, Goëau H, Barbe J, Selmi S, Champ J, Dufour-Kowalski S, Affouard A, Carré J, Molino J-F et al. 2016. A look inside the Pl@ntNet experience: the good, the bias and the hope. *Multimedia Systems* 22: 751–766.

Knapp AK, Smith MD, Hobbie SE, Collins SL, Fahey TJ, Hansen GJA, Landis DA, La Pierre KJ, Melillo JM, Seastedt TR et al. 2012. Past, present, and future roles of long-term experiments in the LTER network. *Bioscience* 62: 377–389.

Kokaly RF, Asner GP, Ollinger SV, Martin ME, Wessman CA. 2009. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. *Remote Sensing of Environment* 113: S78–S91.

Kothari S, Beauchamp-Rioux R, Laliberté E, Cavender-Bares J. 2023. Reflectance spectroscopy allows rapid, accurate and non-destructive estimates of functional traits from pressed leaves. *Methods in Ecology and Evolution* 14: 385–401.

Kuhn M. 2008. Building predictive models in R using the CARET package. *Journal of Statistical Software* 28: 1–26.

Kühn P, Proß T, Römermann C, Wesche K, Bruelheide H. 2024. Using near-infrared spectroscopy to predict nitrogen and phosphorus concentrations of herbarium specimens under different storage conditions. *Plant Methods* 20: 19.

Lavoie C. 2013. Biological collections in an ever changing world: herbaria as tools for biogeographical and environmental studies. *Perspectives in Plant Ecology, Evolution and Systematics* 15: 68–76.

Marsico TD, Krimmel ER, Carter JR, Gillespie EL, Lowe PD, McCauley R, Morris AB, Nelson G, Smith M, Soteropoulos DL et al. 2020. Small herbaria contribute unique biogeographic records to county, locality, and temporal scales. *American Journal of Botany* 107: 1577–1587.

Martin ME, Newman SD, Aber JD, Congalton RG. 1998. Determining forest species composition using high spectral resolution remote sensing data. *Remote Sensing of Environment* 65: 249–254.

Meineke EK, Classen AT, Sanders NJ, Jonathan DT. 2019. Herbarium specimens reveal increasing herbivory over the past century. *The Journal of Ecology* 107: 105–117.

Meineke EK, Davis CC, Jonathan DT. 2018. The unrealized potential of herbaria for global change biology. *Ecological Monographs* 88: 505–525.

Meireles JE, Schweiger A. 2021. meireles/spectrolab: CRAN-v0.0.15. Zenodo.

Meyer C, Weigelt P, Kreft H. 2016. Multidimensional biases, gaps and uncertainties in global plant occurrence information. *Ecology Letters* 19: 992–1006.

Muggeo V, Vm M. 2008. SEGMENTED: an R package to fit regression models with broken-line relationships. *R News* 2: 20–25.

Neto-Bradley BM, Whitton J, Lipsen LPJ, Pennell MW. 2021. Macroevolutionary history predicts flowering time but not phenological sensitivity to temperature in grasses. *American Journal of Botany* 108: 893–902.

Oquab M, Darcet T, Moutakanni T, Vo H, Szafraniec M, Khalidov V, Fernandez P, Haziza D, Massa F, El-Nouby A et al. 2023. DINOV2: learning robust visual features without supervision. *arXiv*. doi: 10.48550/arXiv.2304.07193.

Panchen ZA, Primack RB, Anisko T, Lyons RE. 2012. Herbarium specimens, photographs, and field observations show Philadelphia area plants are responding to climate change. *American Journal of Botany* 99: 751–756.

Park DS, Feng X, Akiyama S, Ardiyani M, Avendaño N, Barina Z, Bärtschi B, Belgrano M, Betancur J, Bijmoer R et al. 2023. The colonial legacy of herbaria. *Nature Human Behaviour* 7: 1059–1068.

Santacruz Endara PG. 2024. The evolution of species specific spectra in a diverse genus of Neotropical trees (Guatteria, Annonaceae) using herbarium specimen. [Conference presentation]. *International Botanical Congress*, 2024, Madrid.

Sapes G, Schroeder L, Scott A, Clark I, Juzwik J, Montgomery RA, Guzmán QJA, Cavender-Bares J. 2024. Mechanistic links between physiology and spectral reflectance enable previsual detection of oak wilt and drought stress. *Proceedings of the National Academy of Sciences, USA* 121: e2316164121.

Schneider FD, Morsdorf F, Schmid B, Petchey OL, Hueni A, Schimel DS, Schaepman ME. 2017. Mapping functional diversity from remotely sensed morphological and physiological forest traits. *Nature Communications* 8: 1441.

Serbin SP, Singh A, McNeil BE, Kingdon CC, Townsend PA. 2016. Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species. *Ecological Applications: A Publication of the Ecological Society of America* 24: 1651–1669.

Soepadmo E, Saw L. (eds). 2000. *Tree flora of Sabah and Sarawak*. Kuala Lumpur, Malaysia: Forest Research Institute Malaysia, Sabah Forestry Department, Sarawak Forestry Department.

Soepadmo E, van Steenis C. 1972. Flora malesiana. Ed. CGGJ Van Steenis. Series 1.

Stasinski L, White DM, Nelson PR, Ree RH, Meireles JE. 2021. Reading light: leaf spectra capture fine-scale diversity of closely related, hybridizing arctic shrubs. *New Phytologist* 232: 2283–2294.

Vargas CA, Bottin M, Sarkinen T, Richardson JE, Celis M, Villanueva B, Sanchez A. 2024. How to fill the biodiversity data gap: Is it better to invest in fieldwork or curation? *Plant Diversity* 46: 39–48.

Venables WN, Ripley BD. 2010. *Modern applied statistics with S*. New York, NY, USA: Springer.

Wheeler Q. 2014. Are reports of the death of taxonomy an exaggeration? *New Phytologist* 201: 370–371.

White DM, Cavender-Bares J, Davis C, Guzman JA, Kothari S, Meireles JE. 2025. Seeing herbaria in a new light: leaf reflectance spectroscopy unlocks predictive trait and classification modeling in plant biodiversity collections. *EcoEvoRxiv*. doi: 10.32942/X29P8B.

Williams LJ, Cavender-Bares J, Townsend PA, Couture JJ, Wang Z, Stefanski A, Messier C, Reich PB. 2021. Remote spectral detection of biodiversity effects on forest biomass. *Nature Ecology & Evolution* 5: 46–54.

Yesson C, Brewer PW, Sutton T, Caithness N, Pahwa JS, Burgess M, Alec Gray W, White RJ, Jones AC, Bisby FA et al. 2007. How global is the global biodiversity information facility? *PLoS ONE* 2: e1124.

## Supporting Information

Additional Supporting Information may be found online in the Supporting Information section at the end of the article.

**Dataset S1** Original spectral data collected for this study.

**Dataset S2** Accession list of specimens observed for this study.

**Fig. S1** Preliminary test of spectral classification with PLS-DA vs LDA.

**Fig. S2** Classification accuracy between close relatives.

**Fig. S3** Joint spectral and Pl@ntNet classification accuracy.

**Notes S1** Effect of close relatives on classification accuracy.

**Notes S2** Using spectra & Pl@ntNet jointly for classification.

**Table S1** Summary of reflectance measurements.

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