Hyperspectral lidar for monitoring high-resolution activity patterns of African stingless bee species

Abstract

Background

Stingless bees are vital pollinators and honey producers in the tropics. Research on stingless bees is generally underrepresented compared to the western honeybees, and while stingless bee studies from some regions are reported, there is a particular lack of reports on the species endemic to Sub-Saharan Africa. Since conventional entomological methods such as mark-recapture and radar harmonic tags suffer from limited observation counts and amount to a significant payload, fluorescent powder tagging offers a promising alternative to understanding their behavior. We deploy a hyperspectral fluorescence lidar monitors a 25-mm-wide transect in front of the hives.

Results

During a 1 day study at the International Stingless Bee Center, near Kakum National Park, Ghana, 17,862 insects were observed with the lidar, of which 7520 were tagged with fluorescent dyes. Approximately half of the bees from the selected hives were successfully tagged, with an estimated misclassification of 1%. According to our limited data, the observed species, Meliponula bocandei and the Dactylurina staudingeri exhibited different activity patterns. D. staudingeri displayed a half-hour longer active day, with clear crepuscular activity peaks. In contrast, M. bocandei activity was diurnal, with less pronounced crepuscular peaks.

Conclusions

We demonstrate how hyperspectral fluorescence lidar can monitor powder-tagged insects throughout the day. The monitored species revealed distinct activity patterns over the day. Our findings highlight the potential of this technology as a valuable tool for understanding insect behavior and environmental preferences of species, in situ, which could potentially give clues of response to climate changes of these critical species.

Introduction

Pollinators, particularly bees, play an indispensable role in supporting wild ecosystems [1] and agricultural production [2]. Honeybees often dominate the discourse on pollination, and their decline is covered in contemporary studies [3, 4]. However, in tropical and subtropical regions worldwide, the stingless bees comprise a majority of all eusocial bees, both in abundance and diversity [5, 6]. Stingless bees contribute to ecosystem biodiversity and bolster food security across Latin and South America, Asia, and Africa by pollinating a diverse range of crops [5, 7, 8]. They face alarming declines worldwide [9,10,11], and as a response, there are global interest in preservation as indicated by governmental and private initiatives [12]. Beyond their role in pollination, stingless bees, through meliponiculture (raising stingless bees), yield products such as honey, cerumen, propolis, and pollen. These products have significant economic and cultural value across societies [9, 13,14,15,16]. Despite their global relevance, there remains a notable research gap concerning stingless bees, particularly in Africa [17].

Understanding the activity patterns of these bees is crucial for discerning their distinct foraging strategies, relevant to tasks such as pollination [18], honey production [19], and social cohesion within wild bee communities [20]. Comparative studies highlight that foraging strategies diverge among bee species. In Brazil, the stingless bee species Melipona subnitida was observed to decrease their pollen foraging round-trip durations during the hottest hours of the day [21]. In Australia, the species Trigona carbonaria Smith's daily activity period was found to be longer during the warmer months [22]. In Costa Rica, Melipona fasciata, M. beecheii, and M. favosa had longer daily foraging periods than Tetragonisca angustula [23]. Such environmental preferences could indicate the species’ responses to upcoming climate changes. However, monitoring with high temporal resolution on multiple individuals is typically hindered by the lack of time-resolved data from conventional research methodologies [24, 25]. Furthermore, to our knowledge, no studies have detailed the daily activity pattern of the endemic African species Meliponula bocandei and Dactylurina staudingeri. Both species are honey producing eusocial bees that thrive across Ghana's diverse ecological zones [26, 27]. M. bocandei typically nests in cavities featuring narrow entrance slits, while D. staudingeri constructs exposed nests on tree branches with multiple entrances [28]. These species play a crucial role in pollinating a variety of crops, including coconut, citrus, mango, cashew, and shea butter, contributing significantly to agricultural biodiversity [29].

Insect behavior analysis involves a wide range of techniques (see Table 1 for an overview). While methods like manual trapping and counting [24] provide foundational data, they can be time consuming and lack fine-scale resolution. Non-tagging techniques such as image-based tracking [30,31,32,33] and digital holography [34] offer insights but may be limited by frame rates and processing demands. Hive entrance monitoring through visual observations and automated video tracking [35, 36], or hive sensors measuring weight, temperature, or vibrations [37] provide robust activity monitoring, but typically focus on the hives or their immediate vicinity. Radar and lidar show great potential for monitoring insect migration at high altitudes [38,39,40] but are typically not applicable for foraging bees close to canopies. Further, their ability to discriminate between species or individuals without additional tagging is limited [41, 42].

Table 1 A comparison of different techniques for bee monitoring

Tagging introduces specificity to entomological studies. Marking has been used for detailed analysis of insect lifespans [43], population densities [44], and dispersal patterns [45]. However, the efficacy of these tags varies. For instance, radio- and harmonic tags [46,47,48] are mainly used for tracing individual insects for long distances, and the payload weight can influence their behavior [49]. Combining fluorescence tagging with lidar was suggested decades ago [50], but had early challenges with observation counts and low signal strength [51]. Recent advancements include passive solar-induced fluorescence imaging. This method employs quantum dots as markers and harnesses the short-wave infrared (SWIR) solar absorption lines. However, its operation is confined to daytime hours and can be influenced by prevailing weather conditions [32]. In contrast, our group has introduced the active laser-induced fluorescence lidar [52], with no such limitations.

In this study, we used fluorescent tagging in combination with a novel hyperspectral lidar to investigate the activity patterns of the African stingless bee species M bocandei and D. staudingeri. We aim to assess the performance of this method for entomological monitoring by a comparative study of two species of stingless bees in Ghana.

Materials and methods

Experiment

This study was conducted at the International Stingless Bee Center (ISBC) near Kakum National Park [53, 54], Ghana (5°20′28.4"N, 1°22′39.4"W) on 17th March 2022. The time of day is presented in solar time, with sunrise at 5:58 AM (91°E) and sunset at 6:03 PM (269°W). Temperature ranged from 26 ˚C to 32 ˚C, there was no rain, clear skies, and wind was below 3 m/s. Beehives of species Meliponula bocandei (M. bocandei), Dactylurina staudingeri (D. staudingeri), Hypotrigona, and Meliponula ferruginea were present on-site. The entrances of two hives were dusted with distinct fluorescent dyes (UV Holi powder, PaintGlow, UK): M. bocandei (green color) and D. staudingeri (red color), such that the bees leaving the entrance were auto-tagged. Dye was reapplied to the entrances with 2-h intervals. These colonies have been maintained at the ISBC for over a decade, and were originally collected from the wild [53]. There was no possibility to alter the positions of hives at the ISBC; hence, measurements were carried out with unmarked hives in the vicinity of the transect.

A hyperspectral fluorescence lidar was used to monitor a 25-mm-wide transect starting 5.8 m from the lidar and terminated on a neoprene board 23 m from the lidar. The lidar beam passed ~ 15 cm in front of both entrances of the tagged beehives located at 11 m and 17 m from the lidar (see Fig. 1). All other hives entrances were at distances > 50 cm from the transect. The lidar was powered by a diesel generator, allowing for 24 h of uninterrupted measurement, see Fig. 1a, b for an overview of the lidar transect and detailed lidar components.

Fig. 1

Experimental setup a The lidar transect at the International Stingless bee center. The two tagged hives and all untagged hives in view are indicated. b Close-up photo of the lidar, highlighting major components

Instrument

The hyperspectral lidar, inspired by [55, 56], was built at the Laser and Fibre Optics Centre (LAFOC) at the University of Cape Coast (UCC) in Ghana. The design and specifications were identical to those used in prior remote sensing fluorescence studies [52, 57, 58], with detailed parts, calibration information, and assembly instructions available in [52]. The lidar featured a 1 W, 405 nm laser diode expanded to Ø25 mm with a collimating lens, followed by a folding mirror for alignment (Fig. 1b). The illuminated air was imaged onto a 200 µm slit by a Ø75 mm achromat by utilizing the “Scheimpflug configuration” and the “hinge rule” [59]. The slit was subsequently imaged onto the broad side of a CMOS image chip (1920 × 1200 pixel, 12-bit), with a range accuracy of 5%. A grating-based spectroscopic insert was positioned between the slit and the camera, providing spectral information in the other direction of the sensor. The spectral range spans approximately 390 nm to 810 nm and is resolved with 70 effective spectral bands. A lock-in detection method [52, 60] was employed for real-time ambient light subtraction, enabling daytime measurements up to 100 m range. The measurement geometry is limited to narrow line transect, which could be problematic for complex habitats. This technique implies that the laser is switched off and on for alternating exposures, making every other exposure contain signal and background, respectively.

Data pipeline

During the 24 h measurement period, 25,982 measurement files were accumulated, each file representing a 3D data cube of intensity values I(x, y, n) in 12-bit. Here, $x\in \left\{1... {x}_{chip}\right\}$, and $y\in \left\{1... {y}_{chip}\right\}$ are the pixel numbers where x_chip = 1920 pixels and y_chip = 1200 pixels are the number of pixels on the (1920 × 1200 pixel) chip. Each file contains $n\in \left\{1... 256\right\}$ exposures, taken consecutively with 7.73 ms exposure time. One file covers 2 s, and the temporal fill factor is 60% due to delays between recorded files.

Range calibration: the range was estimated using the Scheimpflug configuration through

$$r\left(x\right)={{\ell}}_{BL}\text{cot}\left({\Phi }_{slant}+\frac{{\Theta }_{FoV}x}{{x}_{chip}}\right),$$

(1)

where ${{\ell}}_{BL}= 310$ mm is the length of the baseline, ${\Phi }_{slant}=2^\circ$ is the slant angle, and ${\Theta }_{FoV}=6^\circ$ is the angle between the optical axis and the beam expander, as discussed in [61,62,63]. The estimated range spanned from 5.8 m to 120 m.

Wavelength calibration: the wavelength $\lambda \left(x,y\right)$ was estimated through

$$\lambda \left(x,y\right){=\lambda }_{0}+{\lambda }_{span}\frac{y}{{y}_{chip}}+{\delta }_{fan}\frac{x}{{x}_{chip}}\frac{y}{{y}_{chip}}+{\delta }_{bend}{\left(\frac{x}{{x}_{chip}}\right)}^{2},$$

(2)

where ${\lambda }_{0 }\approx 385$ nm is the starting wavelength and ${\lambda }_{span}\approx 440$ nm is the wavelength span. The small coefficients, ${\delta }_{fan}$ and ${\delta }_{bend},$ describe the undesired ‘keystone’ and ‘smile’ distortions [63, 64] that affect spectral registration at different ranges. The coefficients were determined using the known laser wavelength of the first and second diffraction orders at all ranges [52].

Background subtraction: lock-in detection was employed by switching the laser on and off for odd and even exposures. A background-subtracted signal was obtained by linear interpolation [65].

The above calibration procedure allowed each data cube I(x, y, n) to be analyzed and visualized with its range, wavelength, and time information represented in SI units, I(r, λ, t). One example data file is visualized in Fig. 2a in a 3D plot. In this data file the termination board is visible, and five insects were observed, two of which were powder tagged. The termination echo remains static over time at the 23 m range. The wavelength response is the same as the output laser wavelength, a so-called elastic signal at λ_elastic = 405 nm. However, the second diffraction order of the elastic laser wavelength is also mapped at λ_2nd = 810 nm. The termination echo appears as two horizontal lines, one for each diffraction order. The five insect observations manifest as elastic signals of first and second diffraction order. The fluorescence-tagged insects (highlighted in red and green in Fig. 2a) exhibit, in addition to the elastic signals, a green and a red fluorescence peak, respectively.

Fig. 2

Data processing of a measurement file with insect observations. a Intensity values I(r, λ, t) of a measurement file containing five insect observations and the termination echo. Three selected insect observations are highlighted in different colors, an untagged––in violet, a green-tagged––in green, and a red-tagged––in red. The 3D signals are also projected in black to the wavelength–time plane at the bottom of the plot. b The signal-to-noise ratio SNR(t, r) of each range-time element of the measurement file. The selected insects’ spectra are in their respective colors in (c), (d), and (e). The values used to calculate the SNR in Eq. 1 and Eq. 2 are displayed in (c)

An insect detection scheme was established to find all insect observations. First, a region of interest in the range domain was defined as r ∈ [5.8 m… 21 m] to exclude the termination echo from further processing. Then, a signal-to-noise ratio SNR(r, t) was determined for each remaining time-range element by a spectral differentiation method:

$$\begin{array}{c}{\text{SNR}}_{elastic}\left(r,t\right)=\frac{{\left|I\left(r,{\lambda }_{elastic},t\right)\right|}_{\text{max}}-{\left|I\left(r,{\lambda }_{noise},t\right)\right|}_{\text{ median}}}{{\left|I\left(r,{\lambda }_{noise},t\right)\right|}_{\text{max}}-{\left|I\left(r,{\lambda }_{noise},t\right)\right|}_{\text{ median}}},\end{array}$$

(3)

where the maximum and median intensity norms were evaluated at the elastic wavelength λ_elastic and in a noise region surrounding the elastic wavelength, λ_noise = [390 nm … 400 nm, 410 nm … 425 nm]. We defined another SNR measure for the second-order diffracted light,

$$\begin{array}{c}{SNR}_{2nd}\left(r,t\right)=\frac{{\left|I\left(r,{\lambda }_{2nd},t\right)\right|}_{\text{max}}-{\left|I\left(r,{\lambda }_{noise2},t\right)\right|}_{\text{ median}}}{{\left|I\left(r,{\lambda }_{noise2},t\right)\right|}_{\text{max}}-{\left|I\left(r,{\lambda }_{noise2},t\right)\right|}_{\text{ median}}},\end{array}$$

(4)

where λ_noise2 = [790 nm … 805 nm, 815 nm … 825 nm]. The minimum between SNR_elastic and SNR_2nd for each range–time pixel formed SNR(r, t), which is displayed for the example data file in Fig. 2b. Defining the final SNR(r, t) as the minimum of both the first- and second-order SNR reduces misclassification due to noise at the λ_elastic region or λ_2nd, respectively. This process is illustrated in Fig. 2c, where the ${SNR}_{elastic}=93$ and ${SNR}_{2nd}=36$, yielding an SNR of 36 for the insect observation.

The SNR(r, t) was compared to a threshold value SNR_thres, producing a binary mask image B(t, r) = SNR(r, t) > SNR_thres, resulting in B(t, r) = 1 for all (r, t) combinations that include an insect observation and B(t, r) = 0 otherwise. The binary image was then dilated to connect multiple exposures of the same insect. Five observations were found in the example file evaluated in Fig. 2: three are purely elastic, one shows green fluorescence, and one shows red fluorescence. Three of the observations are highlighted in color in Fig. 2a,b; the corresponding spectra of the highlighted observations are found in Fig. 2c, e.

Metadata were saved for each extracted insect observation, and these data include the time and range information and quantification of how much elastic, green fluorescent, and red fluorescent light is detected. The quantification was accomplished by averaging the intensities of the corresponding spectral, range ∆r, and time ∆t regions of the observation,

$${\text{I}}_{\text{elastic}}=\frac{1}{\Delta r \Delta t}\sum_{r=\Delta r}\sum_{\uplambda ={\lambda }_{elastic}}\sum_{t=\Delta t}I\left(r,\lambda ,t\right), {I}_{green}=\frac{1}{\Delta r {\Delta \lambda }_{green}\Delta t}\sum_{r=\Delta r}\sum_{{\uplambda =\Delta \lambda }_{green}}\sum_{t=\Delta t}I\left(r,\lambda ,t\right),{ I}_{red}=\frac{1}{\Delta r {\Delta \lambda }_{red}\Delta t}\sum_{r=\Delta r}\sum_{{\uplambda =\Delta \lambda }_{green}}\sum_{t=\Delta t}I\left(r,\lambda ,t\right).$$

(5)

The spectral regions ∆λ_green = [490 nm … 530 nm] and ∆λ_red = [580 nm … 630 nm] correspond to the fluorescent regions of the green and red powder, respectively. Normalized intensities,

$${\widehat{I}}_{green}= \frac{{I}_{green}}{{I}_{elastic}+{I}_{green}+{I}_{red}},{\widehat{I}}_{red}= \frac{{I}_{red}}{{I}_{elastic}+{I}_{green}+{I}_{red}},$$

(6)

from all observations are shown as a 2D histogram contour plot in Fig. 3. The observations were classified as untagged, green, or red fluorescent based on the values of ${\widehat{I}}_{green}$ and ${\widehat{I}}_{red}$. The classification thresholds are set by identifying the saddle points within the data in Fig. 3.

Fig. 3

Observation classification. A histogram of values of ${\widehat{I}}_{green}$ and ${\widehat{I}}_{red}$ for all observations. The threshold values for classifying an observation as untagged, green, or red fluorescent are visualized by colored regions in the contour plot

Analyzing all measurement files gave statistics on the SNR in the time–range pixels. A histogram of the SNR values is shown in Fig. 4a, where the insect observation and noise values follow two distinct lines in the plot. An SNR threshold value ${SNR}_{thres}$ can be set to a value that excludes most of the noise, as discussed in [65]. The mean, μ, and the standard deviation, σ, of the background subtracted observation spectra and the background from all observations are shown in Fig. 4b.

Fig. 4

Statistical analysis on all insect observations. a Log-log histogram of the SNR values of each time–range pixel in all measurement files during 24 h. An extrapolation of the noise and the selected threshold value are indicated in dashed lines. b The mean and standard deviation of the background subtracted spectra and the backgrounds of all insect observations

The described data pipeline effectively detects insects by differentiating the spectral response around the emitted wavelength, both at the sensor's first and second diffraction orders. Through extensive statistical analysis, a robust threshold is set. The observations are classified with a minimalistic scheme integrating intensity in different spectral regions.

Results

The stingless bee activity during the 24 h measurement period is shown in Fig. 5. A total of 17,862 insects were observed with the lidar, with 7520 (42%) identified as tagged with fluorescent dye. This observation count equates to an average of ~ 1 insect per minute per meter throughout the day. However, this number was at its peak of ~ 50 insects per minute per meter during the morning rush hour (7:45–8:15).

Fig. 5

Insect activity for 24 h. Green-tagged (M. bocandei), red-tagged (D. staudingeri), and untagged insect observations of the 24-h measurement are presented as (a) a contour plot over time of day and range. The sunrise (at 5:58), the meridian (at 12:00), and the sunset (at 18:03) are marked with dashed lines. b The observation density over the range in counts per meter. c The observation frequency over time of day in counts per minute. In (d), the percentage of green-tagged, red-tagged, and untagged insect observations are shown in intervals defined in Table II, close to and far from the two tagged hives and for day and nighttime

The observations are categorized into green-tagged (M. bocandei), red-tagged (D. staudingeri), and untagged, represented in a contour plot as green, red, and gray, respectively (see Fig. 5a). The observation density (counts per meter) and frequency (counts per minute) throughout the day are displayed in Fig. 5b and Fig. 5c. Most of the green-tagged observations are found at the 11 m mark during daylight, while red-tagged observations are mostly found at the 17 m mark. This pattern is consistent with the locations of the tagged hives at 11 and 17 m from the lidar and the diurnal behavior of stingless bees. Due to imperfect powder tagging yield, untagged observations also have activity peaks at these distances, although less pronounced.

The activity patterns in Fig. 5c differ between the two tagged species and the untagged ones. D. staudingeri were observed from 05:50 (8 min before sunrise) to 18:04 (1 min past sunset) with pronounced crepuscular activity peaks (8:00 and 16:30) and an activity minimum at 15:00. M. bocandei showed a 33-min shorter active day (06:12–17:53), with no morning peak but increased activity throughout the day, culminating in a peak at 16:30.

Table 2 defines four different regions of range and time of day. Different classifications of the observations are evaluated within the regions (see Fig. 5d). The tagging yield ranged from 42 to 46% in the first two regions. However, accounting for other insects likely found within the defined ranges (estimated to ~ 300 counts per meter), the tagging yield increased to 51% and 48%, respectively. The misclassification between red and green observations is estimated to ~ 1% using the assumption that there should be no increased activity at the entrance of the other species. This implies that ~ 1% of the bees that were classified as red-tagged, was in reality green-tagged, and vice versa. Far from the hives 4–6% of the observations were tagged, and during nighttime, no tagged observations were recorded, underscoring the high reliability of the classification between elastic and fluorescent observations.

Table 2 Defined ranges and time of day for the ‘intervals’

The observations and analyses presented offer detailed insights into the activity patterns of the two tagged species and untagged insects.

Discussion

Common methods for insect behavior monitoring, such as visual observation, hive sensors, and video tracking, offer valuable insights but often face limitations in specificity, spatial range, or temporal resolution. The hyperspectral lidar method presented here aims to address these challenges by providing high-resolution and species-specific tracking capabilities. Here, we discuss the specific advantages of this lidar technique and areas where it complements, rather than replaces, existing methodologies.

We employed a novel hyperspectral lidar with the capability to monitor insects' activities throughout both day and night along a 23 m transect, in front of their hives. This system detected over 17,000 tagged and untagged insects and provided millisecond temporal and centimeter spatial resolutions. Indeed, the ability to remotely detect small insects relies on the high resolution in time, range, and wavelength with lock-in and differentiation methods. The second diffraction order is usually considered undesirable but is, in our case, utilized to increase the reliability of detections and for continuous wavelength calibration. Although the frame rate is sufficient for distinguishing single observations, systems with kilohertz sample rates can determine, e.g., flight speed [41] and wingbeat frequency [66].

Our methodology of powder tagging has been reported to have little effect on flight behavior [67] compared to bulkier radio transmitters [68]. The labor required for our approach is notably less than conventional counting methods, making it a promising tool for extensive studies.

One constraint inherent to lidar systems is their one-dimensional monitoring. Expanding to 2D or 3D tracking in future work could offer broader insights. This approach could be achieved by scanning or wobbling the beam [57, 58, 69]. Furthermore, our study's confined transect (~ 20 m) restricted our observations to free-flying insects in proximity to the hives. Future research might benefit from exploring longer-range observations. This focused data collection along the transect could be greatly enriched by combining it with visual observations or localized hive monitoring. Such integration would offer insights into flight trajectories, behaviors near the hive entrance, and confirmation of any species identification made by the lidar system.

Since this study is limited in terms of duration and number of hives monitored, we cannot discern to what degree the patterns are influenced by attributes specific to the measuring day and these specific colonies’ sizes and microclimates. A more rigorous study with multiple tagged hives and a longer duration could be explored in the future. However, we resolve distinct activity patterns of the two endemic stingless bee species, which showcases the possibilities of this technology. The variations between the two species, with the smaller D. staudingeri exhibiting a more extended active day yet less active during the warmer hours compared to the larger M. bocandei, offer intriguing ecological insights. Previous literature has noted a heightened heat sensitivity in pollen-collecting stingless bees compared to their nectar-foraging counterparts [21]. While some studies suggest that African endemic stingless bees demonstrate a preference for pollen [70], others specifically identify M. bocandei as primarily nectar foragers [71]. Our findings, which indicate greater heat resilience in M. bocandei, align with this latter observation. In Scotland, the larger bumblebees were found to be more resistant than honeybees to harsh, i.e., cold, weather conditions, leading to longer active days [72]. Although more research is needed to confirm our findings, our results propose that in the tropics the opposite could hold true, insects with larger body sizes could have shorter active days and more activity during the warmest hours.

Conclusions

Our deployment of a novel hyperspectral lidar provided insights into the behavior of over 17,000 tagged and untagged insects. Combining high temporal and spatial resolutions, the technology offers substantial improvements over conventional entomological methods. Notably, the unique activity patterns of the endemic stingless bees, D. staudingeri and M. bocandei, were revealed, offering ecological implications. We hope that the observed distinctness in the activity patterns of these species will aid the establishment of conservation strategies for stingless bees. Our methodology emerges as a promising tool for further ecological studies, increasingly relevant due to impending challenges like climate change and insect decline.