Impact of transmitter mass and shape on beetles

The transmitter's weight has decreased in the last 25 years (Figure 2). In 1996, a study on the ground beetle Carabus coriaceus L. used transmitters weighing between 0.6 and 0.7 g (Riecken & Raths, 1996), whereas today the frequently used tags weigh about 0.3 g or even less (McCullough, 2013; Hamidi et al., 2017). The most important limiting factor for further tag miniaturization is the energy source, i.e., a battery, which is the heaviest part of any transmitter. Smaller batteries mean lighter tags but this also shortens battery life (to a couple of days). Currently (i.e., accessed 12 January 2022), the lightest commercially produced transmitter weighs 0.13 g (model NanoPin, 11 × 3 × 3 mm; Lotek Wireless, Newmarket, ON, Canada), 0.15 g (T15, 11 × 3.4 mm; Advanced Telemetry Systems, Isanti, MN, USA), or 0.27 g (LB-2X, 8 × 4 × 2.8 mm; Holohil Systems, Carp, ON, Canada). The ATS model has a prolonged battery life span (11–22 days) but it has not yet been used for research purposes. Taking into account all these structural features, size and weight constraints still limit the feasibility of radio telemetry in entomological studies, as only few beetle species are large enough to carry the transmitters.

Tags are directly glued on the cuticle of the beetles. For attachment, cyanoacrylate glue in gel or liquid (e.g., Rink & Sinsch, 2007; Negro et al., 2008; Tini et al., 2017b), silicone (Riecken & Raths, 1996), or hot-melt adhesives (McCullough, 2013; Moore et al., 2017) were used. In ground-dwelling apterous beetles (some carabids), transmitters were mounted on the top of elytra. In the case of macropterous species, the pronotum was used for tag fixation in order not to restrict flying (Figure 1). Before attachment, beetles' body temperature could be reduced in a refrigerator (Rink & Sinsch, 2007) or CO₂ was used as an anesthetic agent (Riecken & Raths, 1996).

The transmitter's weight may impact beetle behavior. In contrast to vertebrates (Kenward, 2000), there is no radio-tracking ‘5% rule’ in insects, i.e., the tag mass should not exceed 5% of the body mass of the tracked individual. Observed load ranged from 2% (McCullough, 2013) to 30% (Chiari et al., 2013) in flying species, and from 14% (Elek et al., 2021) to 70% (Deichsel, 2007a) in ground-dwelling species. The lower load in flying species is in accordance with findings by Boiteau & Colpitts (2001), that a load exceeding 33% restricted the flying ability. Although the need to quantify the effects of transmitters on beetles is clear, relevant experiments are rare (Batsleer et al., 2020). In the available literature, the assessment of the tag impact is often not provided or just minimally justified; usually based on simple subjective evaluation of tracked individuals. For instance, some authors reported that radio-tracking likely did not substantially disturb the behavior because some of the tracked individuals were observed walking, flying (Hedin & Ranius, 2002; Drag & Čížek, 2018), mating with untagged partners (Negro et al., 2008), or feeding on earthworms (Růžičková & Veselý, 2016). In the reviewed studies, the load (tag/body mass ratio) does not seem to be the limiting factor for dispersal ability either by walking or flight (Figure 3). However, tag presence may significantly change the proportion of behavioral patterns of tagged individuals – see a case study in Batsleer et al. (2020); although the experiment was conducted on a digger wasp species, a similar impact of tags may occur in beetles, or see Kaláb et al. (2021) for a study on crickets. The origin of beetles also should be considered as a potential bias because laboratory-reared individuals can be more sedentary than wild-type ones (Al Ansi et al., 2020). Hamidi et al. (2017) reported that only 11% of tagged individuals flew normally under laboratory conditions and in the experiment of Al Ansi et al. (2020), the laboratory-reared specimens with attached tags did not fly at all. Similarly, walking speed might be affected and individual responses can be either positive (i.e., hyperactivity) or negative and species-specific (Boiteau et al., 2010). In addition, some authors mentioned that only the largest and heaviest specimens were equipped with radio transmitters due to load reasons and sometimes only (the bigger) females were tracked (Deichsel, 2007a, b; Svensson et al., 2011; Tini et al., 2017c; Thomaes et al., 2018). Due to the dorsal position of the tag, transmitters not only change the individual body mass but also slightly alter the body shape and the center of gravity. We only found one study (Deichsel, 2007b) that examined how this altered body shape changed movement over various obstacles. The author found that tagged beetles had less tortuous trajectories than untagged ones and rested more. There is a rich literature on how tags influence energy balance (e.g., Barron et al., 2010; Wilson et al., 2021) or mortality (e.g., Rasiulis et al., 2014) in vertebrates; similar studies are urgently needed for beetles and other invertebrates.

Localization and sampling intervals

In the field, tagged individuals have always been followed by researchers – but see the potential for automation in Fisher et al. (2020a) and Lee et al. (2021) – that may be time-consuming because a tracking session can take several hours (Riecken & Raths, 1996; Hedin & Ranius, 2002; Tini et al., 2017c). Usually, so-called homing is implemented for localization (White & Garrott, 1990). It is based on tracking whereby the searching starts at the point (fix) where a tagged individual was found during a previous tracking session. The signal intensity guides the researcher to get as close as possible to the tagged individual and its location is recorded (with or without seeing it). Getting too close may be disruptive and, in ground-dwelling beetles, there is a trampling risk that may harm the targeted individual. Depending on the objectives of the study, the number of tracking sessions per day can vary from one (Negro et al., 2017; Drag & Čížek, 2018; Thomaes et al., 2018) to eight (Růžičková & Veselý, 2016, 2018), or the tracking can be conducted every hour in different time shifts to cover all parts of the day (Chiari et al., 2013; Tini et al., 2017a,b,c,). For long-distance movement, tracking once per day seems to be enough to get sufficient data for dispersal estimations, but for the assessment of circadian activity or the effect of abiotic factors (temperature, humidity) on movement patterns, it is important to track beetles as frequently as possible. Moreover, maintaining a regular time schedule between tracking sessions is important for detailed trajectory analyses (Elek et al., 2021).

Signal loss

A robust source of the signal and a good spreading medium are the two major parameters for good signal quality and detection. In the first case, the battery life and the antenna length are key for having a strong signal. In the studies found, the tag antenna (of different lengths) was always directed backward. A longer antenna means a stronger signal and a larger detection range. A tag with a 25-mm-long antenna could be detected up to 60 m (Růžičková & Veselý, 2016), whereas a 100-mm-long antenna had a detection range of approximately 300 m (Rink & Sinsch, 2007). However, the long and soft antenna wire cannot only hamper the movement of the tracked individual but can also be damaged. Burrowing behavior, especially, can cause the antenna to break at multiple points resulting in reduced signal quality (Hamidi et al., 2017; Thomaes et al., 2018). For instance, if the 150-mm-long antenna is reduced to half, the signal range is reduced by more than 75% (Hamidi et al., 2017).

The signal detection may be interfered with by bad weather conditions, especially heavy rains, when the signal is reflected several times from wet surfaces (vegetation) and localization may become almost impossible (Riecken & Raths, 1996). Contact with the individual may be lost completely. In addition, the transmitter may be lost due to digging activity of beetles (Thomaes et al., 2018) or high water/high moisture levels may reduce the quality of the used adhesive material. The signal may also be weakened due to battery problems. For instance, Hedin & Ranius (2002) reported that they lost 71% of females and 20% of males of O. eremita before reaching the end of the nominal lifetime of batteries due to technical issues or water damage. Deichsel (2007a) was unable to recover five out of 11 individuals of C. nemoralis, presumably due to tag malfunction or predation. Al Ansi et al. (2020) lost >66% of tagged individuals due to predation. Dispersal could also create difficulties: Moore et al. (2017) lost 42% of Oryctes rhinoceros (L.) individuals immediately after release because they flew beyond the detection range.

Movement data for analyses

The trajectory made by the tracked individual is usually recorded as a sequence of discrete movement steps that are characterized by two parameters: step length (distance) and turning angle (bearing; Turchin et al., 1991). The exact coordinates of each movement step are mostly recorded manually by various global positioning system (GPS) devices after the localization of the tracked beetle. Although this is a very convenient method, the positional imprecision of GPS devices adds a measurement error to the obtained geocoordinates. The magnitude of this error is affected by several factors including atmospheric interference, topography, canopy cover, or the device itself (Frair et al., 2010; Ranacher et al., 2016). Today's commercially available GPS loggers are extremely accurate at a landscape scale with a precision of approximately 3 m under normal weather conditions (Ranacher et al., 2016). In large-bodied vertebrates, the problems associated with GPS inaccuracy are less important because they move on spatial scales from hundreds to thousands of meters and on a temporal scale, from weeks to years (Frair et al., 2010). However, at smaller spatio-temporal scales (ground-dwelling carabids may move only 50 cm; Riecken & Raths, 1996; Růžičková & Veselý, 2018) and limited battery life of VHF transmitters, even a small degree of GPS imprecision may create a serious bias distorting trajectory shapes (Růžičková & Elek, 2021) and potentially completely mask the biological signal in movement data through erroneous model parameterization (Jerde & Visscher, 2005; Bradshaw et al., 2007). For instance, the measurement error of GPS in fine-scale movements of ground-dwelling beetles was 1.878 m for distance and 31.330° for bearings in the open area under sunny weather (Růžičková & Elek, 2021). Moreover, bearings were significantly more sensitive to measurement errors at short distances. Under dense canopy and rainy weather, the measurement error increased. The alternative recording approach of distance-bearing (also called coordinate geometry) seems to be more appropriate for recording fine-scale beetle movements (Růžičková & Elek, 2021). In the distance-bearing, the coordinates of the target location are obtained from the previously known position using directly measured distance and compass azimuth (Turchin et al., 1991).

The cost of radio-tracking

Radio telemetry is not the cheapest method for tracking, due to the cost of the equipment. This issue rules out researchers with small budgets, including those from low-income countries (Hebblewhite & Haydon, 2010; Weaver et al., 2021). In some cases, researchers can compromise by using a few tracking devices with a risk of reduced data quantity and subsequent problems with publishing. This obstacle can be partially overcome by using relatively low-budget methods, such as direct observation (Drees et al., 2008; Cerda et al., 2015), capture-mark-recapture (Kagawa & Maeto, 2009; Svensson et al., 2011), or fluorescent powder applied on the ground (Perry et al., 2021). Unfortunately, these approaches have considerable limitations for obtaining detailed movement data suitable for trajectory analyses.

Trajectories, distances, and dispersal

The movement (dispersal) capacity of a radio-tracked individual is directly measured by covered distances per time unit. The covered distance is the simplest descriptor reported, also showing differences between sexes (e.g., Chiari et al., 2013; Růžičková & Veselý, 2018) or age classes (Tini et al., 2017a). Especially for saproxylic beetles associated with the occurrence of dead wood, it is important to correctly estimate such distances, because this will indicate the probability of successful dispersal between suitable habitat patches in fragmented landscapes. This, in turn, can guide management, and define the recommended position of ‘stepping stones’ for conservation purposes (e.g., Hedin & Ranius, 2002; Hedin et al., 2008; Rink & Sinsch, 2011). Comparison of the dispersal ability between populations of the same species across its distribution range can be feasible for protected species such as the stag beetle L. cervus (e.g., Rink & Sinsch, 2007; Tini et al., 2017a; Thomaes et al., 2018). A sufficient number of recorded locations (ideally, >10 per individual; Turchin et al., 1991) can provide home range estimates based on minimum convex polygon or kernel density estimations (Tini et al., 2017a). Another option is to map the trajectory, visually evaluate its shape and measure the covered total distance and net displacement, i.e., the direct distance between the first and last recorded positions. The latter characteristic can also be considered as an effective distance as it clearly shows how far a radio-tracked individual can disperse from the point of release. Nevertheless, trajectories can be evaluated in a more sophisticated way by connecting movement parameters with habitat utilization at the individual level. There has been a progressive development in the analysis of animal movement in the last decade (Joo et al., 2020), e.g., in state-switching modeling (McClintock et al., 2020), although not yet used by entomologists (but see Elek et al., 2021). Hidden Markov models (HMM) have recently gained prominence in ecology, especially in interpreting animal movement patterns (McClintock et al., 2020). As movement is driven by underlying behavioral decisions, HMMs can sufficiently capture correlation structure in regular movement data and decompose it into distinct movement states to identify behavioral patterns during foraging or dispersal (Michelot et al., 2016). As a working example of the utilization of these tools, we applied hidden Markov models to segment the movement trajectories of a ground-dwelling beetle into two distinct activity stages: random walk and directed movement (Box 1).

Abiotic factors and habitat use

The effect of various abiotic characteristics on beetle movement is not particularly surprising as beetles are ectotherms. Detailed descriptions of species-specific responses to particular factors are, however, still limited to a few species. Time of day (Negro et al., 2008; McCullough, 2013) and air and ground temperature (Tini et al., 2017b; Růžičková & Veselý, 2018) are the most common factors considered. They are followed by air humidity (Tini et al., 2017b; Bérces & Růžičková, 2019), which can be negatively correlated with the temperature (Elek et al., 2021). The assessment of habitat use is one of the most important aims of radio telemetry studies due to its importance for conservation and management (Kissling et al., 2014). Shapes of trajectories including its length and tortuosity can well describe habitat utilization. The spatial and temporal scales of movement can differ from short steps with frequent turns of ground-dwelling beetles within a habitat (Bérces & Růžičková, 2019) to long-distance dispersal between habitats exceeding a few kilometers (Drag & Čížek, 2018). Some papers focused on movement in different habitats (i.e., meadow vs. forest) to describe preferred sites (Riecken & Raths, 1996; Negro et al., 2008) or even microsites (microhabitats) within a habitat mosaic (Negro et al., 2017; Bérces & Růžičková, 2019). Radio-tracking can be useful in identifying oviposition sites (Hedin et al., 2008; Tini et al., 2017c) or localization of scattered resources, such as hollow logs for breeding or sap runs for feeding (Chiari et al., 2013; Thomaes et al., 2018). Most of the studies were conducted in undisturbed natural or semi-natural habitats. Only one study considered roads as possible barriers for dispersal (Deichsel, 2007a), and two others studied forestry treatments and their direct impact on individual movement patterns of ground-dwelling species (Negro et al., 2017; Elek et al., 2021). Radio-tracking also clarified movement patterns of the red palm weevil between palm trees to identify their cryptic breeding and aggregation sites (Beaudoin-Ollivier et al., 2003; Moore et al., 2017; Al Ansi et al., 2020).

Animal movement may predict dynamics in community structure

Movement data can be useful for the estimation of survival rates, population sizes, or dynamics (Kenward, 2000; Le Gouar et al., 2015). As radio telemetry is gradually becoming available for smaller species, changes in model assumptions might be necessary. Nathan et al. (2008) claimed that progressive technological development might be responsible for the shift from movement analyses based on population redistribution (so-called Eulerian approach) to individual path characteristics (Lagrangian approach; Duchesne et al., 2015). These current trends in movement ecology trigger improvements in analytical techniques (Nathan et al., 2008; Duchesne et al., 2015). Although numerous analytical techniques are available to evaluate internal and external factors shaping movement patterns, only a few of these have standardized and rigorous protocols (Duchesne et al., 2015; McClintock et al., 2020). Studies of the movement of beetles can be suitable for testing whether individual movement parameters of different species can help to understand interspecific and intraspecific interactions in structuring communities and their dynamics (Grüm, 1971a,b; Costa-Pereira et al., 2022). The species composition, richness, and abundance (often referred to as activity-density in ground-dwelling arthropods) are frequently used characteristics of insect/beetle communities. The activity-density is defined as the number of individuals that actively moved and were caught by a pitfall trap (Kotze et al., 2011). This definition gave a behavioral aspect to the interpretation as the activity of the individuals is positively correlated with the number of catches, suggesting a sort of habitat preference. Although this assumption seems logical at first sight, there are several cases when it may be limited due to the demographic structure of the local population, species-specific movement activity, or individual trajectory profiles (Grüm, 1971a,b; Matalin & Makarov, 2011; Růžičková et al., 2021). Grüm (1971a,b) suggested that the differences in movement activity between individuals are based on their physiological condition (chances of food intake) and population density. The rate of metabolism can be linked to population density, thus the movement probability will be higher in dense populations due to density-dependent movement behavior. This increased mobility will be manifested in the increased rate of metabolism. Grüm (1971b) revealed that the directional movement between habitat patches may be compensation for the dissimilarities in energy balance between individuals in habitat mosaics. Moreover, an increased rate of mobility may lead to an increased rate of predation pressure (Grüm, 1971b; Elek et al., 2021), which reduces the population density and may affect individual behavior (Riecken & Raths, 1996). Thus, the observed predator avoidance behavior had an effect on the daily rhythm of the movement activity (Elek et al., 2021), as well as population density in habitat patches. All these clues suggest that the individual perspective may be a manifestation of physiological factors and the current state of the surrounding (micro)environment. Thus, these complex interactions of animals with their environment may manifest themselves in complex movement patterns, suggesting that the individual responses reflect a spectrum of decisions (Okuzaki, 2021).