Journal of Insect Conservation

, Volume 21, Issue 3, pp 573–579 | Cite as

Introducing time-lapse cameras in combination with dataloggers as a new method for the field study of caterpillars and microclimate

  • Matthias DolekEmail author
  • Maria Georgi


We used time-lapse cameras, in combination with dataloggers for microclimate (air humidity and temperature), in an insect field study to analyse behaviour of caterpillars over several larval stages and determine mortality reasons in relation to microclimate. We studied caterpillars of instar 1–3 of the Moorland Clouded Yellow (Colias palaeno, Linnaeus, 1761), that is from hatching from the egg until hibernation. The observation by time-lapse cameras enabled us to gather data on several caterpillars simultaneously over longer time periods. Especially, the combination with dataloggers collecting microclimatic data gives interesting insights in the life, mortality causes and behaviour of the observed caterpillars in relation to microclimatic conditions. To our knowledge, time-lapse or automatic cameras combined with dataloggers collecting microclimatic data have not been used in field studies on phytophagous insects, but only in defined experimental settings or to observe pollinators visiting flowers. Therefore, we summarize our experiences on opportunities and limitations in this communication. If the observed insect is not moving too far and is most of the time visible on the upper surface of the leaf this method can be used for several research questions under very different conditions.


Butterfly Microclimate Caterpillar Behaviour Colias palaeno 


In conservation, it is important to understand habitat requirements of focal species to properly direct conservation actions designed to create their specific habitats. For Lepidoptera it has been shown repeatedly that the larval phase is especially susceptible to habitat changes and thus a crucial part of the life cycle for the long-term survival of populations (Čelik et al. 2015; Dolek et al. 2013; Freese et al. 2006; Höttinger 2004; Myers 1988; van Swaay et al. 2012; Wellington 1957). Caterpillars are usually searched for, presumed explanatory variables measured or estimated and the distribution of caterpillars in space and under observed conditions described to draw conclusions about their preferred microhabitats. Quite often microclimatic conditions play an important part in explaining the distribution (Weidemann 1989) and survival of caterpillars (Fartmann and Hermann 2006). Eilers et al. (2013) e.g. found that females of Pyrgus armoricanus prefer for oviposition food-plants growing in especially warm microclimates, as directly described by temperature measurements (the difference between air temperature and the temperature 1 cm above the ground next to the food-plant) and indirectly by structural parameters influencing microclimate (south-facing exposition, high percentage of bare ground next to the food-plant). In this study, oviposition preferences and habitat patch area explain 65% of the variation in population size. Radchuk et al. (2013) found that all larval stages of Boloria eunomia are effected by temperature and conclude that all life stages have to be considered when assessing the response of the butterfly to climate change. Turlure et al. (2010) studied Boloria aquilonaris in the field and the laboratory. During their field work they revealed that Sphagnum hummocks are an important aspect of habitat suitability for larvae. They observed that larvae retreat into Sphagnum hummocks during hot periods of the day and confirmed in the laboratory that larval survival is lower under hot conditions. Temperature measurements in the field showed, additionally, that Sphagnum hummocks offer stable cool conditions, while temperature fluctuates in hummocks of other mosses (Polytrichum sp.). They concluded that the role of Sphagnum hummocks as a thermal refuge is important for B. aquilonaris and should be maintained by conservation management.

A similar importance of moss undergrowth was found for Colias palaeno with a better survival on food-plants (Vaccinium uliginosum) growing on Sphagnum dominated areas compared to other mosses or litter (Dolek and Freese-Hager 2011). These results were obtained in the field by repeated observation of individual caterpillars. A similar approach was used by Freese et al. (2006): egg-batches and larval webs of Euphydryas maturna were individually marked and repeatedly checked in the field to obtain mortality rates under a range of naturally occurring conditions, which were described by a set of simultaneously measured variables. In summary, this approach of studying individually marked caterpillars or caterpillar webs in the field is applied much more rarely than generalized studies of caterpillars in the field or laboratory studies. One of the important pre-conditions is that caterpillars must be found again, with high precision, when re-visiting the marked spot. This is not possible for all species, some are camouflaged to an extent that re-discovering them on subsequent visits is difficult, e.g. Coenonympha oedippus (Čelik et al. 2015).

While different mortality rates under different conditions were found for C. palaeno repeatedly in several years (Dolek et al. in prep.), it never became directly observable, which mortality factors produce these differences in mortality rates. Hence, we introduced the utilization of time-lapse cameras to fill this gap. As time-lapse cameras have become easily available at low cost, our approach combines time-lapse cameras with dataloggers to measure microclimatic conditions thus producing additional information and describing the behaviour of a caterpillar over several larval stages. Our additional goal was to compare the behaviour of caterpillars of C. palaeno under different microclimate conditions.

The use of automatic cameras and time-lapse cameras in ecology and wildlife management is already widespread (Rovero et al. 2013). Publicly known are photo traps for larger animals such as tapirs (Tapirus indicus, Holden et al. 2003) or smaller mammals and birds (Dodge and Snyder 1960), while insects are filmed in experimental settings in laboratory studies (Aguayo et al. 2010; Fry et al. 2000; Noldus et al. 2002; Schmitt et al. 2014). In contrast, it is not common to use cameras to study insects in the field. Usually, these studies use a specific design to permit camera observation. Meyhöfer (2001) exposed colonies of aphids on sugar beet leaves to predators, Schenk and Bacher (2002) exposed laboratory-reared shield beetles (Chrysomelidae) on their host plants to study predation and Merfield et al. (2004) observed the reaction of Arachnida, Myriapoda, Hymenoptera, Isopoda and Coleoptera to eggs of Calliphora stygia F. (Calliphoridae, Diptera) in two different field margins and in the laboratory. Recently, Lowenstein et al. (2017) used time-lapse cameras to quantify the influence of predators and parasitoids on eggs and larvae of the cabbage looper (Trichoplusia ni). Obviously, these studies focus on sedentary insects and their predators.

Another use of automatic and time-lapse cameras to observe insects is exemplified by Steen (2017), Edwards et al. (2015) and McGimpsey and Lord (2015). Steen (2017) used automatic cameras with a motion detection script to observe and identify pollinators, especially bumblebees, while Edwards et al. (2015) used time-lapse cameras to observe pollinators and flower development. McGimpsey and Lord (2015) investigated colour change of flowers in Euphrasia dyeri and interpreted it as a signal to flower visitors. In these cases, the flower as a resource for the observed insects is sedentary and allows camera observation.

In addition to these studies, we applied time-lapse cameras combined with dataloggers (temperature, air humidity) for the study of caterpillars and their microclimatic living conditions in the field. We found no comparable study researching phytophagous insects, their behaviour, and microclimatic conditions. Here, we would like to present our experiences to summarize opportunities and limitations of this approach.


Study species

The Moorland Clouded Yellow (C. palaeno (L.)) is a univoltine butterfly (Pieridae, subfamily Coliadinae). It is a holarctic species, distributed in several subspecies (Maey 1986) in the boreal region from Central and Northern Europe through Siberia to Korea and Japan as well as in Northern America (Anwander et al. 2013; Higgins and Riley 1978). In most Central European areas, it is strictly tyrphobiontic, restricted to bogs, while above the tree line in the Alps dwarf shrub communities are utilized as habitats. Its wing-span can reach 45 mm, the females are pale yellow, whereas males have a sulfur-yellow color with a broad black margin (Ebert and Rennwald 1991). C. palaeno is endangered in Germany and Bavaria (Voith et al. 2016).

The butterfly is a cold stenothermal species and considered a glacial relict (Tolman and Lewington 2008). Thus, in lower altitudes, the butterfly is confined to raised bogs and blanket bogs, where it occurs at the edges (Settele et al. 2008). The caterpillar is monophagous on bog blueberry (V. uliginosum); out of over 1000 egg and caterpillar field observations only one oviposition and one feeding caterpillar were observed on Andromeda polifolia (Dolek et al. unpubl. data). The flight period lasts from the end of May until the beginning of August. During this period females deposit their eggs on the leaves of the food-plant near the tip of the shoot. The caterpillar hatches after 1–3 weeks and starts feeding on the upper side of the leaves. Caterpillars grow slowly up to instar 3 and may start to move off the plant for hibernation in August. Until hibernation they show a characteristic window feeding, while they consume whole buds and leaves after hibernation. During hibernation, they can endure very low temperatures (Vrba et al. 2014). After the fifth instar, pupation occurs from May onwards.

Time-lapse camera and dataloggers

We used GardenWatchCam (GWC) by Brinno (Taipei City, Taiwan), which is advertised to observe, e.g., the opening of buds of garden flowers. The camera processes the images into a resolution of 1280 × 1024 pixel (HD resolution, avi-format). An inspection of the film at different speed and of each single image is easily possible with the software that comes along with the camera. The camera can be focused on 45 cm distance, it is weatherproof, it can be set to take a picture between every 5 s and every 24 h. We set the cameras to take one picture every minute. Under the macro view option caterpillars can be observed as close as almost 30 cm, although they are a bit out of focus at this short distance. The camera has no IR light for observations during the night. Our study species is day active and we assume that they rest in the night as we found, repeatedly, caterpillars in the morning on the same spot as they were seen in the evening before. We used ten cameras for several weeks in the summer. For the data and results shown in this paper, ten cameras were deployed from August 2–30, 2014. If a caterpillar died, the camera was moved to another spot with a living caterpillar.

As we were interested in caterpillar development under different natural conditions we combined the cameras with dataloggers recording temperature and air humidity. The Keytag KTL-508 dataloggers by Keylog Recorders were used. Beyond datalogger information, the cameras provide further insight into the microclimatic conditions faced by the caterpillars: from the pictures the duration of raindrops or dew-fall present on the leaves can be read thus supplementing air humidity measurements. Knowing from breeding experiments that caterpillars of many species do get diarrhoea from wet food-plants, this could be an important feature. Additionally, when observing the caterpillars, it is also possible to note if they are in the sun (basking, collecting energy) or in the shade (avoiding overheating).

Study area and data collection

Our study was conducted in southern Bavaria, Germany, in the pre-alpine bog and hill area. This area is the most important stronghold of C. palaeno in Bavaria and Germany (Anwander et al. 2013). Altogether, 35 caterpillars were filmed 2014 in two bogs: Schemer Filz (Landkreis Bad Tölz-Wolfratshausen) and Weihermoos (Landkreis Ostallgäu). Because the quality of the videos from Weihermoos was better we choose 14 caterpillars in Weihermoos for the analysis presented in this paper. This bog is situated at an altitude of 845 m above sea level, annual precipitation adds up to 1300 mm (Siuda 2002). The bog is part of a Natura 2000 reserve (8329-302 “Weihermoos Holzleuten”). It developed in a glacial moraine deposition that occurred during the last glacial period (Siuda 2002). The dataloggers and cameras were placed in the south-eastern part of the bog.

Our observations of C. palaeno caterpillars range from instar 1 to instar 3 (approximately 3–9 mm long). We defined three main behavioural categories: feeding, moving and resting (see example pictures in Supplementary Material).

  • Feeding: pictures defined as “feeding” were where the caterpillar removes parts of the leaf and sits in typical feeding position.

  • Moving: this was defined as when a caterpillar’s position was displaced between two images.

  • Resting: this was defined as when the position of the caterpillar sitting at the base of a leaf (or at a stem) did not change. For C. palaeno the position at the base of the leaf is the typical resting position, where they spend a lot of time.

As we set the cameras to take one picture every minute, we missed, however, all behaviour and all behavioural changes shorter than 1 min. Within this time frame our behavioural categories are approximations of the true activity.

Maximum relative humidity and minimum temperature were not normally distributed and we used the Kruskal–Wallis-test and Steel–Dwass post hoc test to compare the behaviour of the caterpillars under different microclimatic conditions.


Out of the 35 observed caterpillars seven survived until the end of the observation period and remained on a leaf near to the ground for hibernation. Five additional caterpillars moved to the ground for hibernation. Two changed their shoot (we re-discovered them later), one died through predator attack, four died through a hailstorm and 16 disappeared. They disappeared early in the observation period, clearly before hibernation, and we assumed mortality for them.

The cameras survived all weather conditions of a Central European summer, including a hailstorm. From direct observations in the field we knew that caterpillars spend a lot of time in their typical resting position and perform distinct longer periods of movement or feeding. For our study, it was thus convenient to take a picture every minute, as the caterpillars of our study species move around little enough to capture their activities in 1 min intervals. Under this scheme (time-lapse setting 1 min) cameras recorded for about 5 days using rechargeable batteries. Setting “night off” did not work reliably. The start of time and date stamp can easily be programmed. It is important to notice, however, that at the first start the camera takes the programed time as real time. The time the cameras first start recording should therefore be exactly at the programmed time.

We hypothesized that resting, in comparison to feeding and moving, takes place during less favourable abiotic conditions. The camera observations confirmed that the minimum temperatures are lower during resting than during feeding, while moving takes place under intermediate minimum temperatures (Fig. 1). For minimum temperature for the three categories of behaviour (F = feeding, M = moving, R = resting) the Kruskal–Wallis test revealed a significant difference in at least one of the behavioural categories (n = 14). The Steel–Dwass post hoc test shows that there is a significant difference between the categories feeding and resting (indicated by “a” and “b” in Fig. 1, Steel–Dwass; n = 14 Z = −2.83; p = 0.0131).

Fig. 1

Minimum temperature for the three categories of behaviour (F = feeding, M = moving, R = resting). Kruskal–Wallis test revealed a significant difference in at least one of the behavioural categories (n = 14), the Steel–Dwass post hoc test showed that there is a significant difference between the categories feeding and resting (indicated by “a” and “b”, Steel–Dwass; n = 14 Z = −2.83; p = 0.0131)

Additionally, the maximum relative humidity differed between the three behavioural categories (Kruskal–Wallis test, nF = 14, nM = 14, nR = 14, df = 2, χ2 = 6.9569, p = 0.0309). The post hoc test revealed that the maximum relative humidity values during feeding and resting differed significantly (Steel–Dwass, n = 14, Z = 2.44; p = 0.0392). Maximum values of relative humidity were higher during resting than during feeding. Caterpillars might be avoiding feeding under wet conditions.

Table 1 summarizes our experiences of how and under which conditions time-lapse cameras can be used. In general, caterpillar size, visibility, and mobility restrict possibilities, while the direct combination of measurements and observations offers additional insights. Of course, some limitations are linked to the specific equipment used and may easily be overcome by improvements such as better macro settings.

Table 1

Summary of opportunities and limitations when observing caterpillars with time-lapse cameras




Size of caterpillar

Possible from ca. 3 mm length, good from ca. 5 mm length. Under good conditions, comparisons of different stages are possible

Small caterpillars are difficult to detect, best under good weather conditions

Size of observed habitat

An area of 50 × 50 cm is easy to observe under macro settings


Movement of caterpillars

Good for relatively immobile species/stages

Mobile caterpillars may often disappear behind leaves or move out of the observed area

Duration of observation

Observation during the day for long periods, depending on weather conditions. The batteries must be changed regularly (if one picture/min. is taken, after 5 or 6 days)

Even rather immobile species may choose to sit on a leaf hidden from view, the camera lens may be fogged, or during rain the picture quality may become low

Location on the leaf

Good for caterpillars sitting in the open on the upper side of the leaf

Not applicable for caterpillars hidden on the under-side of the leaf

Vegetation density

Good in sparse vegetation

Not easily applicable for caterpillars living in dense vegetation

Diurnal activity pattern

Good for day-active species

Not applicable for night-active species, may be overcome by night sensing equipment

Recording microclimatic conditions

Good for assessing water on the leaves and direct sunshine on the caterpillar

If the camera lens is fogged, these microclimatic parameters cannot be clearly determined

Combination with dataloggers/microclimate

The observed behaviour and the microclimate can be linked directly to each other for analysis

Datalogger measurements and conditions caterpillars face may be different, even if only few centimetres apart (e.g. shade vs. sunshine)

General procedure

Direct observation of caterpillars over longer periods and observation of different individuals at the same time

Time consuming analysis of videos. Possible improvements by computerized analysis for organisms contrasting with their environment and real movies


The observation of caterpillars, and insects in general, in the field to determine behaviour under different conditions is sometimes tricky and especially time-consuming. With the availability of automatic and time-lapse cameras several authors have utilized these to improve collected data, especially under a specific experimental design and with sedentary study objects or to study pollinators, where cameras were focused on flowers as a sedentary resource attracting insects (Edwards et al. 2015; Lowenstein et al. 2017; McGimpsey and Lord 2015; Merfield et al. 2004; Meyhöfer 2001; Schenk and Bacher 2002; Steen 2017). In this work, we add the application for the study of phytophagous insects in combination with dataloggers as shown by our sample data (see Fig. 1).

In Table 1 we give an overview on opportunities and limitations of camera observations, which may provide a guideline as to whether camera-based observations may be applicable and helpful in other settings of field conditions. This is clearly open to further improvements, especially some technical improvements already exist, such as computerized analysis of videos, higher picture quality, better macro settings, night sensing equipment.

We discussed, for example, improvements for the analysis of resulting pictures by specific software packages. As our study object is well-camouflaged (green caterpillar on green leaves) and lives in a non-homogenous environment, there was no such solution yet. Depending on the conditions, however, programs for the analysis of videos of insects are available (Noldus et al. 2002). Further sources, such as the company BIORAS ( and the platform Icy (, supply software for time-lapse monitoring of animal behaviour, although they are not specialized on insect movements.

The cameras we used for our study produce a relatively low picture quality (1280 × 1024 pixel, HD resolution, avi-format), which was sufficient for our purposes. This quality was enough, because caterpillars of C. palaeno move slowly and are clearly visible on leaf surfaces (see Supplementary Material). The main shortcoming of the technique is that caterpillars may move to the underside of the leaf or behind other leaves; a better resolution does not help in these cases, but the use of several cameras from different directions could be considered. For us, however, the comparatively cheap Brinno cameras were a good way to apply ten cameras simultaneously with given financial resources. It certainly has to be considered for each specific study, what kind of resolution will be needed. Steen (2017) for example used a camera producing high-resolution images which can be zoomed-in and used for species identification as he wanted to determine flower visitors. He used a Canon digital compact camera with the open-source programme Canon Hack Development Kit (SHDK). Additionally, the Brinno cameras TLC 200 HD and 200 Pro HDR feature some improvements such as manual focus and usage at shorter focal length (1 cm to ∞). These cameras were used by Edwards et al. (2015) and Lowenstein et al. (2017).

To observe nocturnal activity patterns it is possible to use night sensing equipment (e.g. automatic infrared light, Steen 2017) or simply use an internal flash as Suetsugu and Hayamizu (2014) did to investigate the nocturnal floral visitors of orchids. They used a waterproof Pentax camera, the Optio WG-1.



The study was supported by the Bavarian Academy of Nature Conservation and Landscape management (ANL) and it is part of the ANL research project “Development of management strategies for habitats and species of the annexes of the Habitats Directive: Analysis of the reasons for the large-scale decline of C. palaeno”.

Supplementary material

10841_2017_9996_MOESM1_ESM.jpg (32 kb)
MOESM1: For each of the three behavioural categories we present one detail out of one picture of the original time-lapse pictures. Therefore, picture quality and file size are low. 1a: Feeding: Fresh green leaf parts appear, where the caterpillar removed the upper layer of the leaf. Supplementary material 1 (JPG 32 kb)
10841_2017_9996_MOESM2_ESM.jpg (21 kb)
MOESM2: 1b: Moving: The caterpillar is turning around and moving back from feeding to resting position. Supplementary material 1 (JPG 22 kb)
10841_2017_9996_MOESM3_ESM.jpg (19 kb)
MOESM3: 1c: Resting: The cat-erpillar is sitting in its typical resting position at the base of the leaf, head downwards. Supplementary material 1 (JPG 20 kb)

MOESM4: Video example, cut from a longer sequence: The caterpillar (No. 88) on a sunny day moving, resting, and feeding (17.08.2014). After the night, the camera lens is fogged (no observation possible), later, dew-fall is visible on the leaves. The caterpillar is resting a long time and then starts to move and feed. In the evening, it starts to rain. (Note: camera time is 2 hours and 33 minutes ahead of CEST). Supplementary material 1 (MP4 0 kb)


  1. Aguayo DD, Mendoza Santoyo F, De la Torre I, Manuel H, Salas-Araiza MD, Caloca-Mendez C, Hernandez DAG (2010) Insect wing deformation measurements using high speed digital holographic interferometry. Optics Express 18(6):5661–5667CrossRefPubMedGoogle Scholar
  2. Anwander H, Dolek M, Scherzinger C (2013) Hochmoor-Gelbling Colias palaeno (Linnaeus, 1761). In: Bräu M, Bolz R, Kolbeck H, Nunner A, Voith J, Wolf W (eds) Tagfalter in Bayern. Eugen Ulmer, Stuttgart, pp 164–167Google Scholar
  3. Čelik T, Bräu M, Bonelli S, Cerrato C, Vreš B, Balletto E, Stettmer C, Dolek M (2015) Winter-green host-plants, litter quantity and vegetation structure are key determinants of habitat quality for Coenonympha oedippus in Europe. J Insect Conserv 19(2):359–375CrossRefGoogle Scholar
  4. Dodge WE, Snyder DP (1960) An automatic camera device for recording wildlife activity. J Wildl Manag 24:340–342CrossRefGoogle Scholar
  5. Dolek M, Freese-Hager A (2011) Ursachenanalyse zum Rückgang des Hochmoorgelblings (Colias palaeno) in Bayern. Report, Bayerische Akademie für Naturschutz und Landschaftspflege, Laufen/SalzachGoogle Scholar
  6. Dolek M, Freese-Hager A, Geyer A, Balletto E, Bonelli S (2013) Multiple oviposition and larval feeding strategies in Euphydryas maturna (Linné, 1758, Nymphalidae) at two disjoint European sites. J Insect Conserv 17(2):357–366CrossRefGoogle Scholar
  7. Ebert G, Rennwald E (1991) Die Schmetterlinge Baden-Württembergs. Band 1 & 2 Tagfalter I & II. Eugen Ulmer, StuttgartGoogle Scholar
  8. Edwards J, Smith GP, McEntee MHF (2015) Long-term time-lapse video provides near complete records of floral visitation. J Pollinat Ecol 16(13):91–100Google Scholar
  9. Eilers S, Pettersson LB, Öckinger E (2013) Micro-climate determines oviposition site selection and abundance in the butterfly Pyrgus armoricanus at its northern range margin. Ecol Entomol 38(2):183–192CrossRefGoogle Scholar
  10. Fartmann T, Hermann G (2006) Larvalökologie von Tagfaltern und Widderchen in Mitteleuropa: - von den Anfängen bis heute. Kettler, MünsterGoogle Scholar
  11. Freese A, Benes J, Bolz R, Cizek O, Dolek M, Geyer A, Gros P, Konvicka M, Liegl A, Stettmer C (2006) Habitat use of the endangered butterfly Euphydryas maturna and forestry in Central Europe. Animal Conserv 9(4):388–397CrossRefGoogle Scholar
  12. Fry SN, Bichsel M, Müller P, Robert D (2000) Tracking of flying insects using pan-tilt cameras. J Neurosci Methods 101(1):59–67. doi: 10.1016/S0165-0270(00)00253-3 CrossRefPubMedGoogle Scholar
  13. Higgins LG, Riley ND (1978) Die Tagfalter Europas und Nordwestafrikas. Parey, HamburgGoogle Scholar
  14. Holden J, Yanuar A & Martyr DJ (2003) The Asian Tapir in Kerinci Seblat National Park, Sumatra: evidence collected through photo-trapping. Oryx. doi: 10.1017/S0030605303000097 Google Scholar
  15. Höttinger H (2004) Grundlagen zum Schutz von Tagschmetterlingen in Städten. Oedippus 22:1–48Google Scholar
  16. Lowenstein DM, Gharehaghaji M, Wise DH (2017) Substantial mortality of Cabbage Looper (Lepidoptera: Noctuidae) from predators in urban agriculture is not Influenced by scale of production or variation in local and landscape-level factors. Environ Entomol 46(1):30–37PubMedGoogle Scholar
  17. Maey H (1986) Der Hochmoorgelbling Colias palaeno Linnaeus 1761 und seine Unterarten. Löbbecke-Museum, DüsseldorfGoogle Scholar
  18. McGimpsey VJ, Lord JM (2015) In a world of white, flower colour matters: a white-purple transition signals lack of reward in an alpine Euphrasia. Austral Ecol 40(6):701–708CrossRefGoogle Scholar
  19. Merfield CN, Wratten SD, Navntoft S (2004) Video analysis of predation by polyphagous invertebrate predators in the laboratory and field. Biol Control 29(1):5–13CrossRefGoogle Scholar
  20. Meyhöfer R (2001) Intraguild predation by aphidophagous predators on parasitised aphids: the use of multiple video cameras. Entomologia Exp et Appl 100(1):77–87CrossRefGoogle Scholar
  21. Myers JH (1988) Can a general hypothesis explain population cycles of forest Lepidoptera? Adv Ecol Res 18:179–242CrossRefGoogle Scholar
  22. Noldus LP, Spink AJ & Tegelenbosch RAJ (2002) Computerised video tracking, movement analysis and behaviour recognition in insects. Comput Electron Agric 35(2):201–227CrossRefGoogle Scholar
  23. Radchuk V, Turlure C, Schtickzelle N (2013) Each life stage matters: the importance of assessing the response to climate change over the complete life cycle in butterflies. J Anim Ecol 82(1):275–285CrossRefPubMedGoogle Scholar
  24. Rovero F, Zimmermann F, Berzi D, Meek P (2013) “Which camera trap type and how many do I need?” A review of camera features and study designs for a range of wildlife research applications. Hystrix 24(2):148–156Google Scholar
  25. Schenk D, Bacher S (2002) Functional response of a generalist insect predator to one of its prey species in the field. J Anim Ecol 71(3):524–531CrossRefGoogle Scholar
  26. Schmitt C, Rack A, Betz O (2014) Analyses of the mouthpart kinematics in Periplaneta americana (Blattodea, Blattidae) using synchrotron-based X-ray cineradiography. J Exp Biol 217(17):3095–3107CrossRefPubMedGoogle Scholar
  27. Settele J, Kudrna O, Harpke A, Kühn I, van Swaay C, Verovnik R, Warren MS, Wiemers M, Hanspach J, Hickler T (2008) Climatic risk atlas of European butterflies. Pensoft, SofiaGoogle Scholar
  28. Siuda C (2002) Erstellung von Umsetzungskonzepten der Moorrenaturierung im Rahmen des Moorentwicklungskonzepts Bayern: Umsetzungskonzept Weihermoos, Landkreis Ostallgäu. Bayerisches Landesamt für Umwelt, AugsburgGoogle Scholar
  29. Steen R (2017) Diel activity, frequency and visit duration of pollinators in focal plants: in situ automatic camera monitoring and data processing. Methods Ecol Evol 8(2):203–213CrossRefGoogle Scholar
  30. Suetsugu K, Hayamizu M (2014) Moth floral visitors of the three rewarding Platanthera orchids revealed by interval photography with a digital camera. J Nat Hist 48(17–18):1103–1109CrossRefGoogle Scholar
  31. Tolman T, Lewington R (2008) Collins butterfly guide: the most complete field guide to the butterflies of Britain and Europe. HarperCollins, GlasgowGoogle Scholar
  32. Turlure C, Choutt J, Baguette M & van Dyck H (2010) Microclimatic buffering and resource-based habitat in a glacial relict butterfly: significance for conservation under climate change. Global Change Biol 16(6):1883–1893CrossRefGoogle Scholar
  33. van Swaay C, Collins S, Dušej G, Maes D, Munguira ML, Rakosy L, Ryrholm N, Šašić M, Settele J, Thomas J, Verovnik R, Verstrael T, Warren M, Wiemers M, Wynhoff I (2012) Dos and Don’ts for butterflies of the Habitats Directive of the European Union. Nature Conserv 1:73–153CrossRefGoogle Scholar
  34. Voith J, Bräu M, Dolek M, Nunner A & Wolf W (2016) Rote Liste und Gesamtartenliste der Tagfalter (Lepidoptera: Rhopalocera) Bayerns. Bayerisches Landesamt für Umwelt (LfU), Augsburg. Accessed 14 May 2017
  35. Vrba P, Dolek M, Nedvěd O, Zahradníčková H, Cerrato C, Konvička M (2014) Overwintering of the boreal butterfly Colias palaeno in Central Europe. CryoLetters 35(3):247–254PubMedGoogle Scholar
  36. Weidemann H-J (1989) Anmerkungen zur aktuellen Situation von Hochmoor-Gelbling (Colias palaeno L. 1758) und Regensburger Gelbling (Colias myrmidone Esper 1781) in Bayern mit Hinweisen zur Biotop-Pflege. Schriftenreihe des Bayerischen Landesamtes für Umweltschutz 95:103–115Google Scholar
  37. Wellington WG (1957) Individual differences as a factor in population dynamics: the development of a problem. Can J Zool 35(3):293–323CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Büro Geyer und DolekWörthseeGermany
  2. 2.University of FreiburgBad PeterstalGermany

Personalised recommendations