Behavioural mechanisms underlying food-deceptive pollination and neonicotinoid exposure of bumblebees

Abstract Pollination is often used as a classic example of mutualism. However, not all plants produce any food reward for their pollinators. Such non-rewarding pollination strategies are called pollinator deception, as plants take advantage of innate and learned sensory traits that interest their pollinators. Pollinator deception has evolved several times in different plant lineages but reduces the seed production in relation to rewarding plant species. Resource allocation to growth and increased cross-pollination have been both presented as plausible hypotheses for the evolutionary ecology of deceptive pollination. However, it is still unclear if there are ecological factors affecting the profitability of pollinator deception. My aim was to study spatial effects of pollinator deception. I started my studies by monitoring the pollination of a deceptive orchid Calypso bulbosa in natural plant populations. My results show that C. bulbosa competes for pollinator attraction, as increasing neighbour distances and small local populations both positively affected male pollination success. After that I wanted to study similar questions experimentally in controlled settings. For that reason, I developed an automated computer controlled robotic flower system. Using bumblebees as test animals, my results show that pollinators move longer flower-to-flower distances when foraging on deceptive artificial plants compared with rewarding settings, probably increasing cross-pollination in real plant populations. My results also show that in patchy and deceptive settings, a greater proportion of flower visitations are patch-connecting, compared with patchy and rewarding settings. By avoiding trapping the pollinators into distinctive rewarding patches, pollinator deception could increase the gene flow and effective population size in fragmented habitats. During my experiments, I became interested in a timely question related to pollination, the inadvertent effects of neonicotinoid pesticides on pollinator behaviour. My results show that bumblebee’s foraging motivation reduces before any effects on physical performance or learning abilities appear. Such reduction in foraging motivation could partly explain bee pollinator decline, as similar concentrations of neonicotinoids as were used in the study are commonly measured from plant nectar and pollen in agriculturally intensive regions.Tiivistelmä Kasvien ja pölyttäjien vuorovaikutuksia pidetään usein klassisena esimerkkinä mutualismista. Kaikki kasvit eivät kuitenkaan palkitse pölyttäjiään ruualla, vaan huijaavat pölyttäjiä niiden sisäsyntyisillä ja opituilla preferensseillä. Huijaavia pölytysstrategioita on kehittynyt useissa kehityslinjoissa, ja ne ovat erityisen yleisiä kämmekkäkasveilla. Pölyttäjiä huijaavilla kasveilla on keskimäärin kaksi kertaa huonompi siementuotto medellisiin verrattuna. Meden tuottamiseen käytettävän energian allokointi kasvuun ja vähäisempi itsepölytys ovat yleisimpiä hypoteeseja selittämään kuinka pölyttäjien huijaus voisi olla evolutiivisesti vakaa pölytysstrategia. Vielä ei ole kuitenkaan selvyyttä, mitkä ympäristötekijät voivat suosia medettömyyttä. Tässä työssä pyrin tutkimaan vaikuttaako kasvipopulaatioiden spatiaalinen rakenne pölyttäjien huijauksen kannattavuuteen, ja erityisesti onko pölyttäjien huijaus kannattavampaa pirstoutuneissa habitaateissa. Tulokseni osoittavat, että medettömän neidonkengän (Calypso bulbosa) luonnonpopulaatioissa paras pölytysmenestys on, kun neidonkenkäyksilöt ovat kaukana toisistaan ja kun paikallispopulaatiot ovat pieniä. Tämän tutkimuksen jälkeen halusin tutkia vastaavia kysymyksiä kontrolloiduissa olosuhteissa, mitä varten kehitin tietokoneohjasteisen robottikukkasysteemin. Tämän systeemin avulla sain selville, että medettömillä keinokukilla vierailevat kimalaiset lentävät pitempiä matkoja kukasta kukkaan medellisiin verrattuna, mikä tukee ristipölytyshypoteesia. Lisäksi pölyttäjien huijaus laikkumaisissa habitaateissa lisäsi kasvilaikkujen välisiä pölytystapahtumia, mikä voi kasvattaa pirstoutuneiden paikallispopulaatioiden välistä geenivirtaa ja efektiivistä populaatiokokoa. Näiden tutkimusten aikana tulin kiinnostuneeksi minkälaisia käyttäytymismuutoksia torjunta-aineena käytetyt neonikotinoidit aiheuttavat kimalaisille. Tulosteni mukaan kimalaisyksilöiden motivaatio ravinnonkeräykseen heikkenee ennen kuin mitään fyysiseen suorituskykyyn tai oppimiseen liittyviä muutoksia on havaittavissa. Kokeessani käytettyä neonikotinoidipitoisuutta on mitattu yleisesti kasvien medestä ja siitepölystä intensiivisen maatalouden alueilta. Tämä neonikotinoidien aiheuttama heikentynyt motivaatio ravinnonkeräykseen voi osaltaan selittää pölyttäjäkatoa

A low-cost, computer-controlled robotic flower system for behavioral experiments

Abstract Human observations during behavioral studies are expensive, time-consuming, and error prone. For this reason, automatization of experiments is highly desirable, as it reduces the risk of human errors and workload. The robotic system we developed is simple and cheap to build and handles feeding and data collection automatically. The system was built using mostly off-the-shelf components and has a novel feeding mechanism that uses servos to perform refill operations. We used the robotic system in two separate behavioral studies with bumblebees (Bombus terrestris): The system was used both for training of the bees and for the experimental data collection. The robotic system was reliable, with no flight in our studies failing due to a technical malfunction. The data recorded were easy to apply for further analysis. The software and the hardware design are open source. The development of cheap open-source prototyping platforms during the recent years has opened up many possibilities in designing of experiments. Automatization not only reduces workload, but also potentially allows experimental designs never done before, such as dynamic experiments, where the system responds to, for example, learning of the animal. We present a complete system with hardware and software, and it can be used as such in various experiments requiring feeders and collection of visitation data. Use of the system is not limited to any particular experimental setup or even species

Temporal variation of floral reward can improve the pollination success of a rare flowering plant

Abstract Many pollinating animals visit a variety of flowering plant species. Rare plant species pollinated by such generalists may experience a low quality or quantity of pollination, depending on the pollinators’ foraging behaviour. How plants cope with this rarity disadvantage is not well understood. One possibility would be to offer a higher floral reward, for example, a higher nectar sugar concentration. However, since nectar production is costly, rare plants may only be able to increase their nectar concentration for a limited time and offer little reward afterwards. In this study, we performed a laboratory experiment with bumblebees (Bombus terrestris) foraging on artificial flowers of two colours to investigate whether the bees’ foraging behaviour produces a rarity disadvantage and if so, whether the rare flower type could improve its pollination success through temporal variation of its nectar sugar concentration, i.e. a temporary increase of nectar sugar followed by a period with low concentration. We found that when both flower colours offered equal rewards, the rare colour received only slightly fewer visits per flower, but had a considerably lower expected pollination success based on the bumblebees’ visitation sequences. Temporal variation of the rare colour’s sugar concentration increased both the quantity and quality of visits it received. This positive effect was reduced when there were fewer rare flowers or when two bumblebees foraged simultaneously. Our results suggest that temporal variation of floral rewards can alleviate, but not completely eliminate the rarity disadvantage

Low dose of neonicotinoid insecticide reduces foraging motivation of bumblebees

Abstract Widespread use of neonicotinoid insecticides, such as imidacloprid, is often associated with diminishing populations of bees; this loss of pollinators presents a concern for food security and may cause unpredictable changes in ecological networks. However, little is known about the potential behavioural mechanisms behind the neonicotinoid-associated pollinator decline. We quantified the effects of low-dose (1 ppb) imidacloprid exposure on the foraging behaviour of bumblebees (Bombus terrestris). Individual bumblebees were released into a flight arena containing three patches of robotic flowers whose colour (yellow, orange, blue) indicated whether the flower delivered a reward (sugar solution). Exposure to imidacloprid had no significant effect on measures of bumblebee physical performance (such as flight speed) or learning (identifying rewarding flowers). However, pesticide-treated bumblebees had reduced foraging motivation compared with the control bumblebees, as they visited fewer robotic flowers, were slower to start foraging and did not visit all three flower colours as often. Neonicotinoid concentrations of 1 ppb, often reported in plant nectar near agricultural lands, can thus affect the foraging behaviour of bumblebees. Even without a notable impact on flight performance and learning, a reduction in foraging motivation could explain the poor performance of colonies of bumblebees exposed to neonicotinoids

Winters are changing: snow effects on Arctic and alpine tundra ecosystems

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes and biogeochemical cycling. We also compare studies of natural snow gradients with snow manipulation studies, altering snow depth and duration, to assess time scale difference of these approaches. The number of studies on snow in tundra ecosystems has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. In specific, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by manipulative studies (average 7.9 days advance, 5.5 days delay) were substantially lower than those observed over spatial gradients (mean range of 56 days) or due to interannual variation (mean range of 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates

Winters are changing:snow effects on Arctic and alpine tundra ecosystems

Abstract Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates

Winters are changing: snow effects on Arctic and alpine tundra ecosystems

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes and biogeochemical cycling. We also compare studies of natural snow gradients with snow manipulation studies, altering snow depth and duration, to assess time scale difference of these approaches. The number of studies on snow in tundra ecosystems has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. In specific, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by manipulative studies (average 7.9 days advance, 5.5 days delay) were substantially lower than those observed over spatial gradients (mean range of 56 days) or due to interannual variation (mean range of 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates