bioRxiv preprint doi: https://doi.org/10.1101/2020.02.10.941740; this version posted February 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
1 Automated flight interception traps for
2 interval sampling of insects
3 Janine Bolliger 1*, Marco Collet 1,2, Michael Hohl 1, 2, Beat Wermelinger 1, Martin K. Obrist 1
4
5 1 WSL Swiss Federal Research Institute, Zürcherstrasse 111, CH-8903 Birmensdorf
6 2 WSL Institute for Snow and Avalanche Research SLF, Flüelastrasse 11, CH-7260 Davos
7 * corresponding author, [email protected], phone: +41 44 739 23 93, fax: +41 00 739 22 15
8
9 Short title: Automated interval sampling of insects
10
1 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.10.941740; this version posted February 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
11 Abstract
12 Recent debates on insect decline require sound assessments on the relative drivers that may negatively
13 impact insect populations. Often, baseline data rely on insect monitorings that integrates catches over
14 long time periods. If, however, effects of time-critical environmental factors (e.g., light pollution) are
15 of interest, higher temporal resolution of insect observations during specific time intervals are required
16 (e.g., between dusk and dawn). Conventional time-critical insect trapping is labour-intensive (manual
17 activation/deactivation) and temporally inaccurate as not all traps can be serviced synchronically at
18 different sites. Also, temporal shifts of environmental conditions (e.g., sunset/sunrise) are not
19 accounted for.
20 We provide a battery-driven automated insect flight-interception trap which samples insects during
21 seven user-defined time intervals. A commercially available flight-interception trap is fitted to a
22 rotating platform containing seven flasks and a passage hole. The passage hole avoids unwanted
23 sampling during time-intervals not of interest. Comparisons between two manual and two automated
24 traps during 71 nights in 2018 showed no difference in caught insects. An experiment using 20
25 automated traps during 104 nights in 2019 proved that the automated flight interception traps is
26 reliable.
27 The automated trap opens new research possibilities as any insect-sampling interval can be defined. It
28 is efficient and saves manpower and associated costs as activation/deactivation is required only every
29 seven sampling intervals. Also, the traps are accurate because all traps sample exactly the same
30 intervals. The trap is low maintenance and robust due to straightforward technical design. It can be
31 controlled manually or via Bluetooth and smartphone.
32
33 Keywords: biodiversity, ecological impact assessments, insects, inventory, monitoring, sampling
34 optimization, effective sampling
35
2 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.10.941740; this version posted February 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
36 Introduction
37 Passive traps are widely applied in documenting and monitoring insect biodiversity [1, 2]. Among
38 traps, transparent flight-interception traps have been successfully used for many flying insect orders
39 [3-7]. For most trap applications such as monitoring, insect sampling does not require a high temporal
40 resolution and traps are put in place for longer time periods, typically days to weeks [6]. However, in
41 the context of large-scale insect decline, knowledge on how individual environmental drivers act on
42 the populations are needed [8, 9]. One of these drivers, artificial light at night and its mostly negative
43 impacts on insects, has triggered interest among researchers in sampling during defined time intervals
44 (i.e., nights) [10, 11]. While sampling during specific time-intervals is well established for other
45 organisms, e.g. acoustic signals in bats [12], we are not aware of interval-sampling devices for insects.
46 To date, two trap visits per defined time interval were required: trap activation at the beginning of the
47 sampling interval and collection of the insects and deactivation at the end of the time interval. Apart
48 from being labour-intensive, such sampling of time intervals is inherently inaccurate with respect to
49 sampling synchrony because handling the traps takes time and often, experiments consist of numerous
50 traps at different, often distant sites. In addition, seasonal time shifts of narrow time intervals such as
51 sunset or sunrise cannot be accounted for. Lastly, given that vegetation periods are often short anyway,
52 sample size may be even lower than desired because manpower is limited during weekends and public
53 holidays.
54 We present a modified and automated insect flight interception trap whose design, construction and
55 effectiveness allows for sampling user-defined time intervals. While the standard commercial flight
56 interception trap remains to ensure comparable trappability (Polytrap ® https://cahurel-
57 entomologie.com/shop/pieges/434-piege-polytrap.html), we add an automated device to extend on the
58 storing capacity of the sampled insects. The automated sampling device (1) allows for any user-
59 defined sampling intervals; (2) is very efficient as site visits to empty and re-activate the traps are
60 required only after seven consecutive sampling intervals (e.g., even nights); (3) allows for accurate and
61 synchronized exposure times of each of the seven sampling flasks in each trap; (4) is reliable and
3 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.10.941740; this version posted February 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
62 robust due to straightforward mechanical and electronical design and controllable via Bluetooth and a
63 smartphone.
64 Material, methods and results of trap evaluations
65 Trap design
66 Our automated insect trap relies on a commercial flight interception trap (Polytrap ® https://cahurel-
67 entomologie.com/shop/pieges/434-piege-polytrap.html) whose transparent and light plastic (PET)
68 construction consists of two crossed PET sheets which represent an omnidirectional barrier for flying
69 insects, a trap roof, and a funnel and beaker filled with liquid to collect the insects (Fig. 1). These traps
70 are broadly applied in entomological research [5, 6]. While the trappability of the commercial insect
71 trap remains the same, the actual insect sampling is automated and optimized: a platform containing
72 seven flasks is rotated by a stepper motor which is fed by a rechargeable battery (Fig. 2a). The seven
73 flasks contain water - if applicable - mixed with a biocide (e.g., Rocima) and allow to collect insects
74 during seven user-defined time intervals. The time intervals are defined and stored in a table. Using
75 two magnets and a Hall-effect sensor built into the print, the exact position of each flask can be
76 determined. In addition to the seven flasks, the platform contains a passage hole (Fig. 2b). The hole’s
77 position is selected during intervals where insects are not trapped. This ensures that any insect falling
78 into the funnel during non-trapping times is immediately released via the hole (Fig. 2b; see also
79 https://www.youtube.com/watch?v=aGFRxYjO1rM). The insect trap can be connected and controlled
80 via Bluetooth with an App on a smart phone. For this the app BGX Commander needs to be available
81 on the smartphone (Appendix A). Details on the electronic scheme and the state chart of the electronic
82 software are shown in Appendices B and C.
83 The automated insect platform could be built larger and thus contain more flasks than just seven.
84 Sampling seven time intervals represented a trade-off between trap and flask size, trap weight, and
85 servicing intervals. As the traps are serviced only once a week, the flasks had to be large enough to
86 take up enough water in order to counteract evaporation during hot weather periods.
4 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.10.941740; this version posted February 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
87 Initial problems with humidity in the control systems of the traps was overcome by drilling ventilation
88 holes and insulating the electronical parts against humidity (Appendix A).
89
90
91 Figure 1. The automated insect trap mounted on a street-light pole. While the commercially available transparent
92 flight interception trap remains unmodified, the insect sampling unit is optimized: a platform containing seven flasks is
93 rotated by a stepper motor fed by a rechargeable battery. The seven flasks contain a liquid (water with/without biocide)
94 and collect insects during seven user-defined time intervals (e.g., nights).
95
96
5 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.10.941740; this version posted February 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
97
98
6 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.10.941740; this version posted February 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
99
100 Figure 2 (a) Design and components of the platform to optimize insect sampling including a list of required
101 components and their approximate costs. Labor costs are not included; (b) position during periods of sampling (flasks
102 placed underneath funnel) and non-sampling periods, when insects are released through a passage hole.
103
104 Trap evaluation
105 We compared the automated insect interval trap with the original manually operated trap to test the
106 feasibility of the trap’s design and the reliability of the electronical and mechanical components. In a
107 pilot study, insects were caught at street light poles approx. 30 m apart from each other during 71
108 nights between June and mid-July 2018 in the town of Birmensdorf (ZH), Switzerland, using two
7 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.10.941740; this version posted February 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
109 automated insect traps and two manually operated traps. The caught insects were sorted into six
110 groups (Coleoptera, Diptera, Lepidoptera, Hymenoptera, Heteroptera, Neuropterida). In total, we
111 sampled 5716 insects of which 2725 individuals were sampled using the manually operated traps and
112 2991 by the automated traps. There are no systematic differences between the two trap types (Fig. 3).
113 Although comparing only two automated and two manually operated traps do not allow for formal
114 statistical inference, the pilot study confirmed that our approach to automated insect sampling is
115 technically feasible and reliable, resulting in comparable amounts of caught insects. A larger
116 experiment was conducted with 20 automated insect traps during 104 nights in 2019 in the town of
117 Weiningen (ZH), Switzerland. The traps were mounted directly to the street-light poles at a height of
118 ca. 3 m above ground. Between May 20 and August 31 2019, we caught 49’613 insects with an overall
119 mean of about 23 insects per night. Had we only relied on sampling of the nights during the worktime
120 of lab assistants (only Monday-Friday instead of the full seven nights per week), we would have
121 sampled only 60 instead of 104 nights, thus substantially increasing the insect samples by 42%.
122 Overall, only one trap repeatedly failed and had to be replaced. All traps were restarted weekly to
123 readjust their time measurements. Apart from humidity issues in the electronical part of the traps that
124 could be solved (Appendix A), we did not encounter problems and the traps run smoothly for more
125 than three months.
126 Figure 3 Comparison of the mean number of caught insects for automated and manually operated
127 insect traps during 71 nights (data from summer 2018) for two traps.
8 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.10.941740; this version posted February 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
128
129 Discussion and conclusion
130 Insect biodiversity is threatened worldwide and not only specialist species with narrow ecological
131 niches are affected, but also generalist species [8, 13] in protected areas [14]. To develop mitigation
132 strategies for sustainable futures, we need more detailed knowledge on individual drivers of insect
133 decline [8, 9]. As a step forward, we provide an automated insect flight interception trap which allows
134 to sample insects during seven consecutive, user-defined time intervals. The time intervals may allow
135 to identify more precisely the effects of individual environmental drivers such as e.g., street lights
136 which likely impact nocturnal insects. The automated time-interval insect trap addresses several
137 sampling challenges, while ensuring the same trappability as an existing and tested commercial flight
138 interception trap: 1) the trap can be programmed to sample insects during any time interval of interest,
139 e.g., from sunset to sunrise, including adjusted sampling-time intervals to changing day-lengths during
9 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.10.941740; this version posted February 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
140 the sampling period. This enhances sampling accuracy and saves time and personnel costs; 2) the trap
141 connects to smartphones and can be controlled via Bluetooth (Appendix A); 3) as all traps start and
142 terminate sampling automatically and simultaneously, the comparability between (distant)
143 experimental sites is ensured; 4) the straightforward mechanical and electronical design of the
144 sampling units are well accessible and easy to handle; 5) the rotating sampling platform can be
145 mounted to any other trapping device [7].
146 We believe that advantages in the context of increasing environmental stressors, standardized, user-
147 defined sampling intervals become increasingly important and supplement information obtained from
148 long-term insect monitoring. Our automated insect trap reduces maintenance efforts and allows for a
149 broad range of timely applications to assess environmental impacts on insect diversity.
150 The trap is applicable to any research where insect sampling for specified time intervals is asked for.
151 For example, we envision application to identify the natural variation of insect abundance given
152 different treatments such as temperature, precipitation or explicit stressors in an experimental setting,
153 e.g., application of pollutants during different development stages of insects.
154 The traps are with roughly 700 Euros relatively expensive. However, as the automated traps will save
155 considerable personal and maintenance funds, the investment will pay off in the long run. Also, the
156 price of the traps is in the same range as other electronic outdoor devices of the data-logger sector
157 (e.g., https://www.msr.ch/en/product/msr145 for lux, temperature and relative humidity
158 measurements).
159
160 References
161 1. Gibbs J, Joshi NK, Wilson JK, Rothwell NL, Powers K, Haas M, et al. Does Passive Sampling
162 Accurately Reflect the Bee (Apoidea: Anthophila) Communities Pollinating Apple and Sour
163 Cherry Orchards? Environmental Entomology. 2017;46(3):579-88. doi: 10.1093/ee/nvx069.
164 PubMed PMID: WOS:000403079800021.
10 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.10.941740; this version posted February 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
165 2. Russo L, Stehouwer R, Heberling JM, Shea K. The composite insect trap: an innovative
166 combination tap for biologically diverse sampling. PLoS One. 2011;6(6). doi:
167 10.1371/journal.pone.0021079. PubMed PMID: WOS:000291734100051.
168 3. Muller J, Brunet J, Brin A, Bouget C, Brustel H, Bussler H, et al. Implications from large-
169 scale spatial diversity patterns of saproxylic beetles for the conservation of European Beech
170 forests. Insect Conserv Divers. 2013;6(2):162-9. doi: 10.1111/j.1752-4598.2012.00200.x.
171 PubMed PMID: WOS:000316326300007.
172 4. Roth N, Doerfler I, Bassler C, Blaschke M, Bussler H, Gossner MM, et al. Decadal effects of
173 landscape-wide enrichment of dead wood on saproxylic organisms in beech forests of different
174 historic management intensity. Divers Distrib. 2019;25(3):430-41. doi: 10.1111/ddi.12870.
175 PubMed PMID: WOS:000458429600008.
176 5. Benyahia Y, Soldati F, Rohi L, Valladarès L, Maatouf N, Courtin O, et al. First survey of
177 darkling beetles (Coleoptera, Tenebrionidae) of Talassemtane National Park, Western Rif,
178 Morocco. Check List. 2015;11(5):1778. doi: 10.15560/11.5.1778.
179 6. Gossner MM, Lachat T, Brunet J, Isacsson G, Bouget C, Brustel H, et al. Current Near-to-
180 Nature Forest Management Effects on Functional Trait Composition of Saproxylic Beetles in
181 Beech Forests. Conserv Biol. 2013;27(3):605-14. doi: 10.1111/cobi.12023. PubMed PMID:
182 WOS:000319399600021.
183 7. Knuff AK, Winiger N, Klein AM, Segelbacher G, Staab M. Optimizing sampling of flying
184 insects using a modified window trap. Methods in Ecology and Evolution. 2019. doi:
185 10.1111/2041-210X.13258.
186 8. Habel JC, Samways MJ, Schmitt T. Mitigating the precipitous decline of terrestrial European
187 insects: Requirements for a new strategy. Biodiversity and Conservation. 2019;28(6):1343-60.
188 doi: 10.1007/s10531-019-01741-8. PubMed PMID: WOS:000464767600003.
189 9. Rhodes CJ. Pollinator decline - an ecological calamity in the making? Science Progress.
190 2018;101(2):121-60. doi: 10.3184/003685018x15202512854527. PubMed PMID:
191 WOS:000442144100002.
11 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.10.941740; this version posted February 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
192 10. van Langevelde F, Braamburg-Annegarn M, Huigens ME, Groendijk R, Poitevin O, van Deijk
193 JR, et al. Declines in moth populations stress the need for conserving dark nights. Global
194 Change Biology. 2018;24(3):925-32. doi: 10.1111/gcb.14008. PubMed PMID:
195 WOS:000425396700007.
196 11. Owens ACS, Lewis SM. The impact of artificial light at night on nocturnal insects: A review
197 and synthesis. Ecology and Evolution. 2018;8(22):11337-58. doi: 10.1002/ece3.4557. PubMed
198 PMID: WOS:000451611000054.
199 12. Obrist MK, Boesch R. BatScope manages acoustic recordings, analyses calls, and classifies
200 bat species automatically. Can J Zool. 2018;96:939-54. doi: 10.1139/cjz-2017-0103.
201 13. Lister BC, Garcia A. Climate-driven declines in arthropod abundance restructure a rainforest
202 food web. Proc Natl Acad Sci U S A. 2018;115(44):E10397-E406. doi:
203 10.1073/pnas.1722477115. PubMed PMID: WOS:000448713200013.
204 14. Hallmann C, Sorg M, Jongejans E, Siepel H, Hofland N, Schwan H, et al. More than 75
205 percent decline over 27 years in total flying insect biomass in protected areas. PLoS One.
206 2017;12(10):e0185809.
207
208
12 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.10.941740; this version posted February 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
209 Author contributions:
210 JB and MKO identified the need and developed an idea for an automated insect trap. BW provided
211 accessibility to the entomology lab and analysed the insect data. MC and MH conceptualized and built
212 the traps. MC and MH provided the technical figures and tables for the paper. JB, MKO and BW
213 acquired funding for building the automated insect traps. JB provided a first draft of the paper. The
214 authors commented on the drafts and approved the final version of this manuscript.
215 Data availability:
216 We currently do not think that it is of interest to make the insect data collected in 2018 available to the
217 public. If, however, there is a need for this, we will gladly do so.
218
13