8.2: Pre-Lab 8: Pollinators - Biology

8.2: Pre-Lab 8: Pollinators - Biology

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Part I. Pollinators (Refer to Week 8 Reading unless otherwise provided).

  1. How many species of bees are there in the US and Canada?
  1. What is the estimated economic contribution of native pollinators in U.S. crops? And Oregon (find a resource, cite)?
  1. Why is there so much difficulty in finding the information to properly answer #2?
  1. Name three orders of insects that behave as pollinators in the Pacific Northwest, giving a specific example of each.
  1. What is the difference between Acute toxicity, Residual Toxicity and Extended Residual Toxicity?
  1. What steps could farm managers take to improve habitat for bees or to otherwise protect pollinator health? Describe three.

Part II.

Pollination is a mutually beneficial interaction between host plant and pollinator, but no matter how perfect the symbiosis, cheating always evolves.

For example, consider the flower Helicodiceros, the Dead-horse Arum. This is an example of one way plants can trick insects into helping reproduction while providing no reward.

    1. What specific adaptations does this plant have to encourage insect activity?
    2. What insect is the target for this flower?
    3. How does the plant benefit, and why is this an example of “cheating”?

8. What is Pouyannian Mimicry? Give an example of this type of mimicry that is impacting the evolution of bees.

The hypothesis of predator satiation has been proposed to explain mast fruiting in various flowering plants. It considers that the simultaneous production of large numbers of seeds by a plant population reduces the risk of seed predation for each individual. Orchids produce huge numbers of seeds per fruit and rarely experience seed predation. It remains unclear which factors may affect fluctuating fruit production in orchids, which generally suffer a widespread pollen limitation. To explore the temporal pattern of fruiting and potential factors related to fluctuation in fruit production, we investigated reproductive success of a long-spurred orchid (Habenaria glaucifolia) in an alpine meadow with thousands of individuals over 8 years.

To estimate reproductive success, pollinator observation was conducted by day and at night, and pollinia removal and receipt were recorded in the field population for 8 years. To examine whether fruit set and seed set are pollen limited, we conducted supplementary pollination experiments and compared fruit set, seed set and pollinia movement of open-pollinated flowers from 2011 to 2013. We measured lengths of spurs and pollinator proboscises, and nectar volume and concentration, to identify potential pollinators.

Hawkmoths were seen to be effective pollinators for H. glaucifolia in 3 years, whereas in the remaining 5 years no pollinators were observed, and consequently pollinia were rarely transferred. Numerous pollinia movements were observed in 2012, 2013 and 2014 (pollinia removal: 48, 59 and 85% pollinia receipt 51, 70 and 80%), and correspondingly fruit set was significantly higher in 2012 and 2013 (59 and 46%) than in 2011 (25%). It was fruit set, rather than seed set, that was pollen limited in this orchid in the 3 years, in that supplementary pollination increased fruit set but did not increase seed set per fruit compared to natural. Three species of hawkmoths had proboscis lengths that matched the spur length of H. glaucifolia. Fruit set in this long spurred orchid depends on the activity of long-tongued hawkmoths, resulting in significant temporal variation in fruit production. Mast fruiting in this alpine orchid could be attributed to a ‘sit and wait’ strategy, awaiting an abundance of effective pollinators.

Plant Production and Protection (Biology) – Topic Area 8.2

NOTE: The Solicitations and topics listed on this site are copies from the various SBIR agency solicitations and are not necessarily the latest and most up-to-date. For this reason, you should use the agency link listed below which will take you directly to the appropriate agency server where you can read the official version of this solicitation and download the appropriate forms and rules.

The objective of this topic area is to examine novel ways of enhancing crop production and protection by applying biological approaches to develop new methods for plant improvement, apply traditional plant breeding methods and new technologies to develop new food and non-food crop plants, develop plant characteristics that reduce the harmful impact of plant pests and biotic stresses, as well as new genotypes of existing crop plants with characteristics that allow for their use in new commercial applications.

FY 2019 Research Priorities:
Examples of appropriate subtopics for research applications from small businesses include, but are not limited to the following:

1. Plant improvement
Improved crop production using traditional plant breeding and biotechnology, including but not limited to, molecular biology, and mutagenesis, genomics, tissue culture, and/or embryogenesis to produce crops with new or improved quality, yield, agronomic, horticultural, value- added, and/or economic traits. Topics may include, but not limited to:
a. Improvement of commercial floriculture production - Biological and/or technological approaches to improve the competitiveness of U.S. production of flowering potted plants, bedding plants, seasonal crops, annuals, perennials, and cut flowers.
b. Development of new crops - Development of new crop plants as sources of food, non-food industrial or ornamental products.

2. Pollinators and crop production
Projects that address the health and success of domesticated and natural pollinators of economically important crops.

3. Plant protection against abiotic and/or biotic stresses
Reduced the impact of plant pathogens, arthropod pests, and abiotic stress on crop plants and increasing plant resistance to plant pathogens, arthropod pests, and abiotic stress. Topics may include, but are not limited to:
a. Improved plant disease diagnostics - Accurate, rapid, and cost-effective identification of causal agents in specialty crop plants at the earliest possible stage relative to manifestation of disease.
b. Bio-Based approaches - To protect organically-grown and conventional crops from insect and nematode pests and diseases using bio-based approaches, including the development of decision aid systems that are information extensive and time sensitive.

Review Questions

Which of the following components is not used by both plants and cyanobacteria to carry out photosynthesis?

What two main products result from photosynthesis?

  1. oxygen and carbon dioxide
  2. chlorophyll and oxygen
  3. sugars/carbohydrates and oxygen
  4. sugars/carbohydrates and carbon dioxide

In which compartment of the plant cell do the light-independent reactions of photosynthesis take place?

Which statement about thylakoids in eukaryotes is not correct?

  1. Thylakoids are assembled into stacks.
  2. Thylakoids exist as a maze of folded membranes.
  3. The space surrounding thylakoids is called stroma.
  4. Thylakoids contain chlorophyll.

Predict the end result if a chloroplast’s light-independent enzymes developed a mutation that prevented them from activating in response to light.

  1. G3P accumulation
  2. ATP and NADPH accumulation
  3. Water accumulation
  4. Carbon dioxide depletion

How are the NADPH and G3P molecules made during photosynthesis similar?

  1. They are both end products of photosynthesis.
  2. They are both substrates for photosynthesis.
  3. They are both produced from carbon dioxide.
  4. They both store energy in chemical bonds.

Which of the following structures is not a component of a photosystem?

  1. ATP synthase
  2. antenna molecule
  3. reaction center
  4. primary electron acceptor

How many photons does it take to fully reduce one molecule of NADP + to NADPH?

Which complex is not involved in the establishment of conditions for ATP synthesis?

From which component of the light-dependent reactions does NADPH form most directly?

Three of the same species of plant are each grown under a different colored light for the same amount of time. Plant A is grown under blue light, Plant B is grown under green light, and Plant C is grown under orange light. Assuming the plants use only chlorophyll a and chlorophyll b for photosynthesis, what would be the predicted order of the plants from most growth to least growth?

Plants containing only chlorophyll b are exposed to radiation with the following wavelengths: 10nm (x-rays), 450nm (blue light), 670nm (red light), and 800nm (infrared light). Which plants harness the most energy for photosynthesis?

  1. X-ray irradiated plants
  2. Blue light irradiated plants
  3. Red light irradiated plants
  4. Infrared irradiated plants

Which molecule must enter the Calvin cycle continually for the light-independent reactions to take place?

Which order of molecular conversions is correct for the Calvin cycle?

Where in eukaryotic cells does the Calvin cycle take place?

Which statement correctly describes carbon fixation?

  1. the conversion of CO2 into an organic compound
  2. the use of RuBisCO to form 3-PGA
  3. the production of carbohydrate molecules from G3P
  4. the formation of RuBP from G3P molecules
  5. the use of ATP and NADPH to reduce CO2

If four molecules of carbon dioxide enter the Calvin cycle (four “turns” of the cycle), how many G3P molecules are produced and how many are exported?

  1. 4 G3P made, 1 G3P exported
  2. 4 G3P made, 2 G3P exported
  3. 8 G3P made, 1 G3P exported
  4. 8 G3P made, 4 G3P exported

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    8.2 The Light-Dependent Reactions of Photosynthesis

    By the end of this section, you will be able to do the following:

    • Explain how plants absorb energy from sunlight
    • Describe short and long wavelengths of light
    • Describe how and where photosynthesis takes place within a plant

    How can light energy be used to make food? When a person turns on a lamp, electrical energy becomes light energy. Like all other forms of kinetic energy, light can travel, change form, and be harnessed to do work. In the case of photosynthesis, light energy is converted into chemical energy, which photoautotrophs use to build basic carbohydrate molecules (Figure 8.9). However, autotrophs only use a few specific wavelengths of sunlight.

    What Is Light Energy?

    The sun emits an enormous amount of electromagnetic radiation (solar energy in a spectrum from very short gamma rays to very long radio waves). Humans can see only a tiny fraction of this energy, which we refer to as “visible light.” The manner in which solar energy travels is described as waves. Scientists can determine the amount of energy of a wave by measuring its wavelength (shorter wavelengths are more powerful than longer wavelengths)—the distance between consecutive crest points of a wave. Therefore, a single wave is measured from two consecutive points, such as from crest to crest or from trough to trough (Figure 8.10).

    Visible light constitutes only one of many types of electromagnetic radiation emitted from the sun and other stars. Scientists differentiate the various types of radiant energy from the sun within the electromagnetic spectrum. The electromagnetic spectrum is the range of all possible frequencies of radiation (Figure 8.11). The difference between wavelengths relates to the amount of energy carried by them.

    Each type of electromagnetic radiation travels at a particular wavelength. The longer the wavelength, the less energy it carries. Short, tight waves carry the most energy. This may seem illogical, but think of it in terms of a piece of moving heavy rope. It takes little effort by a person to move a rope in long, wide waves. To make a rope move in short, tight waves, a person would need to apply significantly more energy.

    The electromagnetic spectrum (Figure 8.11) shows several types of electromagnetic radiation originating from the sun, including X-rays and ultraviolet (UV) rays. The higher-energy waves can penetrate tissues and damage cells and DNA, which explains why both X-rays and UV rays can be harmful to living organisms.

    Absorption of Light

    Light energy initiates the process of photosynthesis when pigments absorb specific wavelengths of visible light. Organic pigments, whether in the human retina or the chloroplast thylakoid, have a narrow range of energy levels that they can absorb. Energy levels lower than those represented by red light are insufficient to raise an orbital electron to a excited (quantum) state. Energy levels higher than those in blue light will physically tear the molecules apart, in a process called bleaching. Our retinal pigments can only “see” (absorb) wavelengths between 700 nm and 400 nm of light, a spectrum that is therefore called visible light. For the same reasons, plants, pigment molecules absorb only light in the wavelength range of 700 nm to 400 nm plant physiologists refer to this range for plants as photosynthetically active radiation.

    The visible light seen by humans as white light actually exists in a rainbow of colors. Certain objects, such as a prism or a drop of water, disperse white light to reveal the colors to the human eye. The visible light portion of the electromagnetic spectrum shows the rainbow of colors, with violet and blue having shorter wavelengths, and therefore higher energy. At the other end of the spectrum toward red, the wavelengths are longer and have lower energy (Figure 8.12).

    Understanding Pigments

    Different kinds of pigments exist, and each absorbs only specific wavelengths (colors) of visible light. Pigments reflect or transmit the wavelengths they cannot absorb, making them appear a mixture of the reflected or transmitted light colors.

    Chlorophylls and carotenoids are the two major classes of photosynthetic pigments found in plants and algae each class has multiple types of pigment molecules. There are five major chlorophylls: a, b, c and d and a related molecule found in prokaryotes called bacteriochlorophyll. Chlorophyll a and chlorophyll b are found in higher plant chloroplasts and will be the focus of the following discussion.

    With dozens of different forms, carotenoids are a much larger group of pigments. The carotenoids found in fruit—such as the red of tomato (lycopene), the yellow of corn seeds (zeaxanthin), or the orange of an orange peel (β-carotene)—are used as advertisements to attract seed dispersers. In photosynthesis, carotenoids function as photosynthetic pigments that are very efficient molecules for the disposal of excess energy. When a leaf is exposed to full sun, the light-dependent reactions are required to process an enormous amount of energy if that energy is not handled properly, it can do significant damage. Therefore, many carotenoids reside in the thylakoid membrane, absorb excess energy, and safely dissipate that energy as heat.

    Each type of pigment can be identified by the specific pattern of wavelengths it absorbs from visible light: This is termed the absorption spectrum . The graph in Figure 8.13 shows the absorption spectra for chlorophyll a, chlorophyll b, and a type of carotenoid pigment called β-carotene (which absorbs blue and green light). Notice how each pigment has a distinct set of peaks and troughs, revealing a highly specific pattern of absorption. Chlorophyll a absorbs wavelengths from either end of the visible spectrum (blue and red), but not green. Because green is reflected or transmitted, chlorophyll appears green. Carotenoids absorb in the short-wavelength blue region, and reflect the longer yellow, red, and orange wavelengths.

    Many photosynthetic organisms have a mixture of pigments, and by using these pigments, the organism can absorb energy from a wider range of wavelengths. Not all photosynthetic organisms have full access to sunlight. Some organisms grow underwater where light intensity and quality decrease and change with depth. Other organisms grow in competition for light. Plants on the rainforest floor must be able to absorb any bit of light that comes through, because the taller trees absorb most of the sunlight and scatter the remaining solar radiation (Figure 8.14).

    When studying a photosynthetic organism, scientists can determine the types of pigments present by generating absorption spectra. An instrument called a spectrophotometer can differentiate which wavelengths of light a substance can absorb. Spectrophotometers measure transmitted light and compute from it the absorption. By extracting pigments from leaves and placing these samples into a spectrophotometer, scientists can identify which wavelengths of light an organism can absorb. Additional methods for the identification of plant pigments include various types of chromatography that separate the pigments by their relative affinities to solid and mobile phases.

    How Light-Dependent Reactions Work

    The overall function of light-dependent reactions is to convert solar energy into chemical energy in the form of NADPH and ATP. This chemical energy supports the light-independent reactions and fuels the assembly of sugar molecules. The light-dependent reactions are depicted in Figure 8.15. Protein complexes and pigment molecules work together to produce NADPH and ATP. The numbering of the photosystems is derived from the order in which they were discovered, not in the order of the transfer of electrons.

    The actual step that converts light energy into chemical energy takes place in a multiprotein complex called a photosystem , two types of which are found embedded in the thylakoid membrane: photosystem II (PSII) and photosystem I (PSI) (Figure 8.16). The two complexes differ on the basis of what they oxidize (that is, the source of the low-energy electron supply) and what they reduce (the place to which they deliver their energized electrons).

    Both photosystems have the same basic structure a number of antenna proteins to which the chlorophyll molecules are bound surround the reaction center where the photochemistry takes place. Each photosystem is serviced by the light-harvesting complex , which passes energy from sunlight to the reaction center it consists of multiple antenna proteins that contain a mixture of 300 to 400 chlorophyll a and b molecules as well as other pigments like carotenoids. The absorption of a single photon or distinct quantity or “packet” of light by any of the chlorophylls pushes that molecule into an excited state. In short, the light energy has now been captured by biological molecules but is not stored in any useful form yet. The energy is transferred from chlorophyll to chlorophyll until eventually (after about a millionth of a second), it is delivered to the reaction center. Up to this point, only energy has been transferred between molecules, not electrons.

    Visual Connection

    What is the initial source of electrons for the chloroplast electron transport chain?

    The reaction center contains a pair of chlorophyll a molecules with a special property. Those two chlorophylls can undergo oxidation upon excitation they can actually give up an electron in a process called a photoact . It is at this step in the reaction center during photosynthesis that light energy is converted into an excited electron. All of the subsequent steps involve getting that electron onto the energy carrier NADPH for delivery to the Calvin cycle where the electron is deposited onto carbon for long-term storage in the form of a carbohydrate. PSII and PSI are two major components of the photosynthetic electron transport chain , which also includes the cytochrome complex. The cytochrome complex, an enzyme composed of two protein complexes, transfers the electrons from the carrier molecule plastoquinone (Pq) to the protein plastocyanin (Pc), thus enabling both the transfer of protons across the thylakoid membrane and the transfer of electrons from PSII to PSI.

    The reaction center of PSII (called P680 ) delivers its high-energy electrons, one at the time, to the primary electron acceptor , and through the electron transport chain (Pq to cytochrome complex to plastocyanine) to PSI. P680’s missing electron is replaced by extracting a low-energy electron from water thus, water is “split” during this stage of photosynthesis, and PSII is re-reduced after every photoact. Splitting one H2O molecule releases two electrons, two hydrogen atoms, and one atom of oxygen. However, splitting two molecules is required to form one molecule of diatomic O2 gas. About 10 percent of the oxygen is used by mitochondria in the leaf to support oxidative phosphorylation. The remainder escapes to the atmosphere where it is used by aerobic organisms to support respiration.

    As electrons move through the proteins that reside between PSII and PSI, they lose energy. This energy is used to move hydrogen atoms from the stromal side of the membrane to the thylakoid lumen. Those hydrogen atoms, plus the ones produced by splitting water, accumulate in the thylakoid lumen and will be used synthesize ATP in a later step. Because the electrons have lost energy prior to their arrival at PSI, they must be re-energized by PSI, hence, another photon is absorbed by the PSI antenna. That energy is relayed to the PSI reaction center (called P700 ). P700 is oxidized and sends a high-energy electron to NADP + to form NADPH. Thus, PSII captures the energy to create proton gradients to make ATP, and PSI captures the energy to reduce NADP + into NADPH. The two photosystems work in concert, in part, to guarantee that the production of NADPH will roughly equal the production of ATP. Other mechanisms exist to fine-tune that ratio to exactly match the chloroplast’s constantly changing energy needs.

    Generating an Energy Carrier: ATP

    As in the intermembrane space of the mitochondria during cellular respiration, the buildup of hydrogen ions inside the thylakoid lumen creates a concentration gradient. The passive diffusion of hydrogen ions from high concentration (in the thylakoid lumen) to low concentration (in the stroma) is harnessed to create ATP, just as in the electron transport chain of cellular respiration. The ions build up energy because of diffusion and because they all have the same electrical charge, repelling each other.

    To release this energy, hydrogen ions will rush through any opening, similar to water jetting through a hole in a dam. In the thylakoid, that opening is a passage through a specialized protein channel called the ATP synthase. The energy released by the hydrogen ion stream allows ATP synthase to attach a third phosphate group to ADP, which forms a molecule of ATP (Figure 8.16). The flow of hydrogen ions through ATP synthase is called chemiosmosis because the ions move from an area of high to an area of low concentration through a semi-permeable structure of the thylakoid.

    Link to Learning

    Visit this site and click through the animation to view the process of photosynthesis within a leaf.


    Study species and sites

    Q. indica Linn (Combretaceae) is an Asian tropical climber that is mainly distributed in southwest China, including Sichuan, Yunnan, Guizhou, Hunan, Guangxi and Guangdong 24 . The plant is also cultivated in China as an ornamental and its seeds are used medicinally to kill intestinal parasites. In Yunnan Province, the region in which our studies were conducted, Q. indica flowers from April until June. The majority of field observations and experiments on Q. indica was conducted at two locations over a three-year period (2012–2014). The first site was in a liana collection of XTBG (21°45′N, 101°02′E 580 m above sea level), dominated by Lagerstroemia tomentosa (Lythraceae) and Ficus callosa (Moraceae). Q. indica was cultivated for many years at this site and also naturalized into the adjacent limestone forest (semi-natural habitat). The second site was approximately 10 km away in MLD that belongs to Menglun Nature Reserve (natural habitat) (21°55′N, 101°19′E 590 m above sea level) that is dominated by Ficus langkongensis (Moraceae). We also conducted phenological observations and experiments in CQTL (29°50′N, 106°03′E 400 m above sea level), which is known for the cultivation of Q. indica for Chinese medicine (cultivation habitat).

    Floral biology and phenology

    Preliminary phenological observations in natural populations as well as the flowering and fruit set monitoring of cultivated plants were conducted in XTBG in 2012–2014. We randomly selected 30 inflorescences (1–3 per plant) in four study plots and recorded the total number of flowers and duration of flowering. Additionally, we estimated the number of pollen grains and ovules in one flower per inflorescence. We used a haemocytometer to estimate pollen production per flower as described by Dafni 44 . We used pollen and ovule numbers to calculate the mean P/O of the flowers. To investigate floral colour change, we monitored five flowers from different inflorescences every 2 h for 2 d and recorded the time and flower colour, which was assessed visually and then measured with a spectrophotometer (Ocean Optics, USA). In addition, we also measured the petal length of three flowers from each inflorescence of 30 plants in the white, pink and red floral stage using vernier calipers (0.01 mm Guanglu, China) to determine any changes in the corolla size.

    We used 0.1% 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide to test the presence of dehydrogenase as an assay for pollen viability 45 . A total of 64 flowers from four study plots were covered using nylon mesh bags prior to anthesis to prevent insect visits and then four flowers were used to test pollen viability every 3 h from anthesis to flower wilting. The same assay was used to assess stigma receptivity 45 , using the same number of experimental flowers from the same plants. All tests were carried out under favourable weather conditions.

    Every day, we randomly selected three inflorescences from each of three study plants located approximately 500 m apart and covered them with nylon net bags in the afternoon prior to anthesis. Three flowers in each inflorescence were used to measure nectar secretion every 3 h from 20:00 to 17:00 the following day. The nectar volume was measured with 10 and 20 μl ‘micro-cap’ calibrated capillary tubes (Sigma-Aldrich, USA) and nectar sucrose concentration with a hand-held, temperature-compensated refractometer (Bellingham + Stanley Ltd., UK). This experiment was repeated for 3 d (10–12 April 2012) and nine inflorescences with a total of 27 flowers were evaluated. At the same time, the temperature and humidity were measured with a hygrothermograph (0.1 °C, Wangyunshan-Fuzhou, China).

    Flower visitors and pollinators

    We observed flower visitors to Q. indica in 2012 and 2013 in MLD and XTBG for a total of 81 h and 144 h, respectively, under favourable weather conditions. Observations were made continuously from 20:00 (beginning of anthesis) to 8:00 the following morning by video recording during the night and visually during the day. We recorded the total number of visitors per individual flower and the number of flowers visited by each kind of insect. The visiting frequency of each kind of insects was calculated as the number of visits per flower per hour. We assigned insects as visitors or pollinators, based on their behaviour and likelihood of mediating pollination. Visitors were insects observed on inflorescences, whereas pollinators were insects that consistently contacted both anthers and stigmas and had pollen grains deposited on their bodies. We photographed every type of flower visitor and voucher specimens of insects were preserved in the insect collections of XTBG.

    Field manipulative experiment

    To investigate the reproductive contribution of different floral colour stages, we conducted a manipulative field experiment in the XTBG and CQTL populations using inflorescences exposed to natural pollination. We set up four treatments with 30 plants per treatment using 1–3 randomly chosen inflorescences per plant. All inflorescences were covered with nylon net bags to prevent insect visits prior to treatment. The treatments included inflorescences exposed to natural pollination in (1) the white floral stage, (2) the pink floral stage, (3) the red floral stage and (4) all floral colour stages (control). Two months later, when fruits were mature, we counted the fruit set of inflorescences.

    Controlled pollination experiment

    We performed five pollination treatments on 1–3 inflorescences of 30 randomly selected Q. indica plants in the XTBG and MLD populations in 2013 and 2014 to examine the capacity for autonomous self-pollination and evaluate the importance of insect visitors. The treatments were: (1) open-pollination (control), (2) hand selfing, (3) hand outcrossing, (4) hand geitonogamous pollination and (5) bagging (autonomous self-pollination). For each treatment, except for the control, we covered inflorescences with bags prior to anthesis to prevent pollinator access and the flowers were emasculated to prevent self-pollination. We performed hand cross-pollinations using pollen from plants up to 500 m away and randomly selected from 10 inflorescences. Not all flowers within an inflorescence were cross-pollinated because of the technical difficulties related to bud emasculation in this species.

    Collection and identification of floral scents

    Floral scents were collected in situ from flowers in the white, pink and red stage from four Q. indica plants using the dynamic headspace adsorption method 46 and also included inflorescences with a combination of white and red flowers or pink and red flowers. For each collection, an inflorescence with 5–25 flowers was covered with mesh bags to prevent pollinator access. Some leaves close to the inflorescence were removed from the branch at least 24 h prior to the scent collection experiment. For each sample, the average number of flowers used in the white, pink and red stage was 16, 16 and 15.3, respectively. We enclosed a whole inflorescence within an odourless polyethylene terephthalate bag (Kalle Nalo GmbH, Germany) for the volatile collection. Airflow was maintained through the bag by a battery-driven air pump (Qihai Machinery and Electric Co., China). The air was purified by active charcoal, introduced into the bag at a flow rate of 400 ml min −1 , pumped out of the bag through a glass cartridge (7 mm internal diameter) and filled with 300 mg of Super Q adsorbent (80–100 mesh size ARS Inc., USA) at a flow rate of 300 ml min −1 . The adsorbent filters were eluted three times with 100 μl of hexane. To detect any environmental contamination during the volatile collections, ambient air was collected at the same place using the same dynamic headspace technique. The leaf branch was used as control in some cases that only a few leaves were included in the inflorescent treatment. The collection was carried out for approximately 3 h in each floral colour stage. Any volatile compounds that were common with the control treatment were removed before the analysis. To calculate the absolute amount of floral volatiles, two internal standards, octane and decyl acetate, were added to each sample as described by Chen et al. 46 . The extract samples were then stored at −20 °C until analysis.

    The extracts were analysed using a coupled gas chromatography mass spectrometer (GC-MS) system (Agilent, USA) equipped with an HP-5MS column (30 m × 250 μm × 0.25 μm). Helium was the carrier gas and ionization occurred by electron impact (70 eV source temperature 230 °C). For each sample, 1 μl was injected at an injector temperature of 250 °C. The initial column temperature was 40 °C and then increased by 3 °C min −1 up to 100 °C, by 5 °C min −1 up to 200 °C and finally by 20 °C min −1 up to 250 °C, which was maintained for 10 min. Compound identification was based on matching mass spectra in the NIST08 Mass Spec library and confirmed by the retention index (RI) in the NIST online library ( The absolute values of all compounds were estimated using the average peak area of the two internal standards as a reference scale. The relative proportions of each compound were also calculated.

    Data analysis

    To assess the differences of corolla diameter between floral colour stages, we carried out one-way analysis of variance (ANOVA) in conjunction with Fisher’s least significant difference test to identify pairwise differences. The mean fruit set was calculated in all treatments before statistical analysis. We used Kruskal-Wallis test to identify differences in fruit set rates among different treatments in XTBG and CQTL in 2013 and independent-sample t test to identify differences in fruit set under different pollination treatments in XTBG and MLD in 2013 and XTBG in 2014. All analyses were performed using SPSS 20.0 (IBM, USA). A matrix of the relative proportions of all the detected compounds in the three floral colour stages was used to conduct multivariate analysis in R 3.0.0 ( with the ‘vegan’ package 47 . Scent composition among different floral colour stages was also compared. MRPP was performed based on the matrix of mean dissimilarities with 999 permutations to test the null hypothesis (scent profiles among floral colour stages had no differences). NMDS was used to find the best two-dimensional representation of the distance matrix. To evaluate each configurations that produces the distance matrix, we tested different stress values. The fit of the produced distance matrix to the observations increased with the decreasing stress values. These analyses were repeated until two similar configurations with minimum stress values were obtained.

    Electronic supplementary material is available online at

    Published by the Royal Society. All rights reserved.


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    Flowering phenology is a key trait in the reproductive ecology of plants. There is evidence for abundant quantitative genetic variation for flowering time and adaptive differentiation among populations of many species in relation to climatic gradients ( Colautti and Barrett 2013). However, less is known about the factors governing flowering phenology in large multispecies assemblages, particularly in tropical and subtropical communities. Using information from herbarium specimens collected from 2059 species between 1920 and 2007 in a subtropical forest in Guangdong Province, Pei et al. (2015) investigate differences in flowering phenology among trees, shrubs, herbs and vines, and consider the role of environmental and historical factors in shaping the patterns revealed by their analyses. Correlations between abiotic factors (temperature, rainfall and sunshine) and patterns of flowering indicate that climatic factors are probably most important in governing flowering time however, their analyses suggest that phylogenetic constraints also play a role. This study highlights the immense store of valuable biological information in the herbaria of China that awaits future investigations.

    Range margins can provide novel insights into ecological and evolutionary processes because biotic and abiotic factors affecting mating and dispersal commonly differ from the center of the range ( Hargreaves and Eckert 2014). For species involved in co-evolved mutualisms, changed environmental conditions along geographical gradients may be particularly important because old partnerships may break down resulting in the emergence of new interactions. This appears to have happened in Ficus squamosa (Moraceae), a dioecious fig in which a host shift involving its wasp pollinator is evident at the range limit of the species in southern Yunnan. Liu et al. (2015) provide evidence that a new species of pollinating wasp, recruited from the co-occurring but unrelated F. heterostyla, has replaced its usual partner and examine why this may have occurred. The findings of this study are of particular interest because they suggest ways in which fig species may establish novel fig-wasp partnerships and demonstrate that, at least in some species, pollinator sharing can occur.

    One of the main goals of the new field of ecological genomics is to determine the molecular basis of adaptation by finding the specific genes involved and documenting their patterns of variation and expression ( Barrett and Hoekstra 2011 Stinchcombe and Hoekstra 2008). Plant reproductive characters provide a rich source of biological diversity for this type of study. Flowering time is a key life-history trait and considerable progress has been made in recent years in understanding the selective forces governing selection on flowering time as well as the genes and their interactions that are responsible (Arabidopsis thaliana Caicedo et al. 2004 Stinchcombe et al. 2004). Li et al. (2015a) report a preliminary study of two geographically separated populations of invasive Ambrosia artemisiifolia (Asteraceae), previously shown to differ in flowering time consistent with climatic adaptation. They compare gene expression in eight genes known to influence flowering in other species and find significant differences in expression between the populations. Although preliminary, this study provides a valuable starting point for more detailed future investigations. Ambrosia artemisiifolia was introduced to China in the 1930s and therefore research on the molecular basis of contemporary climatic adaptation is of particular interest.


    The seven benefit functions—for biodiversity, carbon storage, flood control, forage production, pollination, recreation, and water provision—have distinctly different spatial distributions, although some areas are of high value to multiple services and other areas are of low value to many (Figure 2). For example, the largest agricultural valley in the ecoregion, Salinas Valley, is characterized by the following: a wide swath of high-value pollination services driven by the high proportion of land under crops benefiting from animal pollination a narrow area of high–flood control services due to riparian vegetation and low values of other services. Similarly, the mountain ranges throughout the ecoregion are characterized by natural forest cover (accounting for carbon storage values), high precipitation (water provision), and proximity to major population centers and accessibility by road (recreation). Accordingly, the following areas share high values for carbon storage, recreation, and water provision: the Santa Cruz Mountains, the Santa Lucia Mountains along the Big Sur Coast, and the northern Diablo Range.

    The seven benefit functions (feature values) are displayed in color with the accompanying best networks of selected planning units in gray insets. Feature values range from 0 (or locked out white), to low (light blue), moderate (dark blue), and high (purple). The boundary indicates the ecoregion plus the 10-km buffer. Yellow lines indicate stratification units, within which individual targets were pursued. Numbers in the thousands (×000) are stratification unit labels. Not shown are planning-unit–specific constraints and stratification-unit–specific targets.

    The spatial correlations between the ecosystems services are low (Figure 3A), with nearly as many negative correlations as positive ones. The overall average correlation is positive but low (0.08). The average correlation between biodiversity and services is also low. The highest correlation is between carbon storage and water provision (0.58). Other relatively high correlations (>0.2) are between recreation and water provision (reflecting the importance of natural cover to both) and between recreation and flood control. The latter correlation reflects the combination of the benefits of natural cover, the accessibility of riparian areas by road (recreation), and the importance of those areas for flood control. Although riparian areas are also important for water quality and aquatic diversity, neither of these was treated explicitly, so the value of riparian areas is underrepresented in our analysis. Negative correlations are restricted to pollination and forage production with other features.

    Associations are expressed as (A) the correlation coefficient (r) between feature values (B) actual/expected number of planning units shared between best networks and (C) actual number of shared planning units as a percentage of the smaller network. Arithmetic averages for each ecosystem service with each other ecosystem service are also noted. Shading indicates strength of correlation/overlap, as indicated by the legends. The shading in (C) follows values in (B). All correlations are statistically significant except for pollination with recreation, flood control, and biodiversity. All overlaps are statistically significant by G-tests for goodness of fit.

    The pair-wise overlap between the seven networks derived by MARXAN for individual benefit functions (individual networks) is displayed in Figure 3B. In most comparisons, the number of shared planning units is more than expected, but in some cases (i.e., with pollination and recreation, and pollination and forage production), there are far fewer shared prioritized planning units than expected. The same overlaps are shown as percentages of the smaller of the two relevant networks in Figure 3C. In many cases, these overlaps are considerable fractions of the smaller networks, exceeding 50% in several cases and 30% in most, so they are substantial in practical terms.

    The seven individual networks are summed over space in Figure 4, highlighting the distribution of areas selected for different numbers of benefits. Some highly urbanized areas are not selected by MARXAN for any benefits (e.g., San Francisco, San Jose, and the northwest corner of Santa Clara County, in gray), in part because we excluded highly developed lands from some service networks and in part because such lands simply do not provide high levels of services or are unsuitable for management for services.

    Colors represent the number of features for which each planning unit was selected in the individual-service best MARXAN network. We selected 1.8% of planning units for ≥5 features and 8.5% for ≥4.

    Other areas, such as San Luis Obispo County, are selected for only a few benefits (in light blue). Although much of San Luis Obispo County is agricultural, there are few high-value crops that benefit from animal pollination. Furthermore, because of the sparse forest cover, this county has relatively low values for carbon storage. Although livestock values are relatively high in San Luis Obispo County, neighboring Kern County has far higher values likely because of nearby feedlots, slaughterhouses, and transportation routes. Accordingly, Kern dominates the network for forage production (Figure 2E). A portion of the planning units, for example in the northern Diablo Range (Figure 4, in pink), were selected for multiple benefits. The value of this area for carbon, water, and recreation is explained above. In addition, the relatively intact oak woodlands are important to biodiversity, forage production, and—owing to the proximity to dense population in San Jose—flood control. Interestingly, both this hotspot of overlap and the hotspot in the North Santa Lucia Range are areas where considerable public land has already been protected.


    The extent to which individual benefit-function targets are met by the four comprehensive networks is depicted in Figure 5A: Biodiversity Non-biodiversity All and Strategic (all except forage production and pollination, which removes all negative correlations and overlaps see Methods). The “Biodiversity” network would protect a considerable supply of ecosystem services. All four networks achieve the carbon storage targets, but none achieves the water provision target (set at only 40% of total water use), and only one (“Non-biodiversity”) achieves more than 60% of the pollination target.

    Target achievement is represented as the proportions of the seven targets achieved by four different conservation scenarios: Biodiversity (only biodiversity) Non-biodiversity (all except biodiversity) All and Strategic (all but forage production and pollination: biodiversity, carbon storage, flood control, recreation, and water storage).

    (A) The average target achieved (achieved feature/target) across stratification units weighted by amount of target, capped at 1 where targets were exceeded.

    (B) The average amount by which targets were exceeded, weighted by target. The total target achievements and surpluses, summed across ecosystem services, appear enclosed in square brackets in the legend. This unweighted total underrepresents the contribution of biodiversity (which alone had hundreds of features, compared with one for each ecosystem service per stratification unit) to the planning process.

    Because target achievement was assessed at the stratification-unit level and then aggregated to the ecoregion level, there are services for which there are considerable surpluses despite unmet targets (e.g., water provision Figure 5B). In other cases, targets are well met, with surpluses (e.g., recreation, flood control) and without (e.g., carbon storage). The inability of “Non-biodiversity” and “All” to appropriately protect biodiversity demonstrates the risks to biodiversity associated with diluting the focus of conservation efforts without expanding the funds available for conservation. Such risks are greatly diminished when the ecosystem services targeted are chosen strategically (as in “Strategic”).

    Coincidence and Side Benefits

    There are major differences in the extent to which benefit-function targets could be met through the biodiversity network alone or with additions. The pollination targets are only 49% met by biodiversity, but they only need 10% additional land (Table 2). This additional 10% contributes relatively little to biodiversity targets. Yet if protection or restoration of natural habitat adjacent to farms pays off entirely through pollination-augmented agricultural profits [13], these biodiversity benefits might come through strategic partnerships without the expenditure of conservation dollars.

    Results from Adding Individual Ecosystem Service Targets to the Existing Biodiversity Network

    Contrast this situation with recreation, for which targets are 82% met by the biodiversity network. To achieve the remaining 18%, we need 9% additional land, which has far greater benefits for biodiversity. Because so much recreation would be provided by the biodiversity network, additional recreation funding could potentially contribute to conservation.

    Carbon targets are met entirely by the biodiversity network because sites with high carbon storage are crucial for forest conservation. Carbon credits applied to forests in California would offer new funding for key elements of the biodiversity network, just as they offer promise in developing nations [85].

    Finally, water provision targets are only 48% met by the biodiversity network, and the 21% additional land is highly valuable for biodiversity, both in total and per hectare. If this biodiversity value of lands valuable for water provision is a common phenomenon, great biodiversity benefits could accrue from the estimated 13% of terrestrial land that might be managed for urban water use [64].

    Key Insights

    As human impacts on the environment expand in intensity and extent, there is a critical need to understand the degree of intersection between conservation priorities for biodiversity and for ecosystem services. This intersection of conservation priorities could achieve a measured and thoughtful balance between previously competing goals, while providing new sources of funding for its full-scale implementation.

    The striking result of this preliminary analysis is the apparent contradiction between results of the spatial association and side benefit analyses. On the one hand are low correlations between the spatial distribution of the ecosystem-service benefit functions and relatively low levels of overlap between prioritized sites (Figure 3). However, despite the generally low correlations, there are hotspots where high values of multiple benefits coincide (Figure 4), although biodiversity protection was not strongly positively associated with any service (Figure 3). Protecting regions selected for their biodiversity value is not likely to maximize protection of the full suite of benefits unless there are considerable changes to the process by which biodiversity priorities are determined.

    On the other hand, the biodiversity network would protect impressive supplies of ecosystem services (Figure 4). But networks configured to maximize the full suite of benefits could do even better (Figure 4). The relatively low overlaps between sites that are most appropriate for different features when prioritized separately do not negate the possibility of considerable gains from simultaneous prioritization: sub-optimal but valuable sites may coincide. Developing methodologies for such combined planning analyses should be a top research priority.

    We adapt a general planning framework for biodiversity to planning for ecosystem services and do not present detailed representations of individual ecosystem services. A much deeper analysis is warranted. The coarse scale of the readily available data for many of the ecosystem services hinders analysis of ecosystem services. The carbon pool and pollination analyses require data with finer resolution within relevant boundaries (Figure 2C and 2D). For example, because most agricultural data are provided by political units such as counties, assessing pollination services requires interpolating fine-scale patterns from coarse-scale data, which likely introduces error. Despite the data limitations, this analysis yields five key insights for individual services, the relationships among them, and the exercise as a whole.

    1. Suitability and demand are determining factors.

    As with biodiversity planning, the network design process for ecosystem services is strongly influenced by factors other than patterns of biophysical supply. Prioritized sites generally have high feature values, but two other factors determine planning unit selection: suitability (lower in urban areas) and targets (intended to represent demand and specific to stratification units, outlined in dark gray in Figure 2). High feature-value sites are not selected for two reasons: low suitability for conservation and low pertinent demand or need. For example, the site-selection algorithm did not select some sites of high forage production in Kings and Kern counties, whereas it did select some low–forage-production sites in San Luis Obispo and Fresno Counties. The former sites have lower suitability due to agriculture and urban development, whereas the latter are more remote.

    Remoteness is relevant for water provision also, where targets are linked to actual water use. Here, several planning units in the Santa Cruz Mountains (which have high precipitation stratification unit 5,000, Figure 2G) are not selected for water provision due to the relatively low demand compared to adjacent sites. Spatial mismatches between supply and demand complicate ecosystem-service provision and the planning for those services.

    2. Spatial scale.

    Two important points pertain to the issue of spatial scale. First, benefits vary in the scale of their operation and dependence on habitat, and this may dramatically affect simultaneous management for multiple services. Most strikingly, biodiversity conservation generally requires large intact landscapes, but crop pollination arises from small patches of (semi-) natural habitat within a human-dominated landscape (we did not consider long-term sustainability of pollinators, which might require larger patches). Not surprisingly, the two features are negatively associated spatially (Figure 3), and each seems to greatly constrain the target achievement of the other in combined networks (Figure 5).

    Second, independent scales of supply and demand can affect relationships between target achievement and the total size of benefit demand and supply. Targets may be poorly met despite relatively high overall availability or they may be well met despite barely adequate availability. Targets are more easily met if demand occurs at broad scales and supply varies considerably at local to regional scales. For example, carbon storage demand is global, but supply varies greatly based on vegetation cover and climatic conditions consequently, it was possible to meet the target of 50% of the ecoregion's carbon storage in all networks (Figure 5). Although global demand makes it easier to meet regional targets, it also introduces artificiality: specific regional targets do not make much sense, because they ignore how well the global targets might be better met elsewhere. In contrast, when demand varies at smaller scales than supply, spatial mismatches are exacerbated and targets may be more difficult to achieve. For example, water demand accompanies agricultural use and residential development, which do not coincide spatially with areas of high water surpluses (precipitation minus evapotranspiration). Although water provision targets were easily met and exceeded in some stratification units (1,000, 5,000, and 6,000 see Figure 2G), they could not be met or even approached in others (2,000 and 3,000, even with a relatively low target of 40% of total water use).

    3. Population centers yield tensions.

    For some ecosystem services, demand scales positively with the number of people in close proximity, whereas developed and agricultural lands are less productive or less suitable for management. These two factors result in a tension in planning, even for an individual service. For example, the demand and therefore the value of recreation opportunities is much greater close to cities (e.g., in the San Francisco Bay area, Figure 2F). When people have alternative sources of outdoor recreation (e.g., South Bay area, in and around San Jose, Figure 2F), the high value may be countered by low suitability (high costs of land management for recreation) such that high value sites are not selected by our method. When people have no other options (e.g., North of San Francisco, Figure 2F), however, the high value supersedes low suitability, and the planning units are prioritized. Similarly, flood control and water provision services are more needed near cities, but are generally degraded by development.

    4. Need new data, methods development.

    To plan thoroughly for multiple ecosystem services, we need considerable advances in data and planning methodologies. Although there was sufficient data in this ecoregion for a first-pass analysis, planning for ecosystem services at smaller scales and in other ecoregions will likely require new research. For example, planning for crop pollination at finer scales requires an improved understanding of the contribution of individual pollinator species to particular crops, which is currently sparsely understood [86–88]. In other places, we anticipate that the kinds of data collected by relevant government agencies in California does not yet exist.

    Although the application of MARXAN yielded insights, the tool lacks several features that are required for ecosystem-service planning. First, a new tool should allow a single network to include different features with different suitability layers. Specific suitability would reflect the factors that affect that particular feature's management. This would allow simultaneous planning for terrestrial and aquatic diversity.

    Second, a new tool should incorporate the possibility that targets will not be met with available resources or that they may be met from outside the planning region. Third, an ideal tool would incorporate some spatial and temporal dynamics to account for the potential impacts of management and threats on species and services. Ideally, conservation would target areas for protection based on the potential for loss of benefits, not simply for the benefits supplied under current land use as in this analysis. Such dynamics should also allow the representation of the dependence of ecosystem functions on changes in biodiversity although these effects might not be generally strong for ecosystem stocks and fluxes, they are likely more important for stability [89].

    Fourth, such a tool should account for the fact that management for one purpose (e.g., threatened species) will be incompatible with management for another purpose (e.g., recreation). Fifth, a tool must account for the flow from particular ecosystems to particular beneficiaries. Site-selection software like MARXAN assigns value to a planning unit in the context of the larger stratification unit, without more specific accounting of spatial context or ecological processes [90]. For example, in modeling the contribution of natural vegetation cover to flood control, we accounted for the proximity to the floodplain and for the population density in the relevant watershed's floodplain, but we could not specifically account for the population downstream that would be directly impacted by flood mitigation.

    Finally, a tool should allow flexibility between the ends of benefit maximization (used by Naidoo and Ricketts [91]) and suitability-maximizing target achievement (used here), which will each be appropriate for individual ecosystem services in different circumstances. Benefit maximization will be especially appropriate when services have substitutes whose appropriateness will also vary spatially suitability maximization will be appropriate for features like biodiversity and perhaps recreation, for which the motivation for protection is principle rather than private preference [92]. Such flexibility will allow more effective analysis and the increased potential for engaging partners whose interests in the full suite of ecosystem services will differ.

    5. Need multidisciplinary and transdisciplinary teams.

    Ecosystem-service planning must involve multidisciplinary and transdisciplinary teams. Interdisciplinarity (research between disciplines) is not sufficient, because ecosystem-service research and planning requires deep knowledge within—and across—multiple disciplines. Planning for ecosystem services requires expertise in biology, chemistry, physics, economics, finance, geosciences, geography, and particular analytical tools. The integration of theoretical understanding and empirical expertise from these diverse fields therefore requires a multidisciplinary team of experts working in close communication, spearheaded by transdisciplinary scholars and practitioners.

    6. Consider trade-offs and side benefits.

    Only by analyzing both the trade-offs and the side benefits for biodiversity of conserving ecosystem services and vice versa can we guide conservation efforts more effectively. Trade-off analyses will be applied most successfully when management for an ecosystem service cannot help to meet the targets of biodiversity conservation.

    Analyses of the ancillary benefits of an ecosystem-service project to biodiversity conservation and vice versa have two purposes. Such analyses can reveal when an ecosystem-service project offers promise for attracting new conservation partners and funds for biodiversity projects, and when such projects are especially important for their biodiversity benefits.

    By combining trade-off and side-benefit analyses with a thorough scoping of potential partnerships and new markets, we may achieve substantial increases in biodiversity conservation while conserving the ecosystem services critical for human well-being. For example, case studies of water regulation and delivery and flood control reveal that conservation of forests and wetlands are sometimes worthwhile from an ecosystem-service perspective alone (in the Yangtze River watershed, China [14], around the Panama Canal [93], and in the Catskills and Charles River watersheds, US [94]). There are other places where such ecosystem-service values are undervalued or not quite sufficient to outweigh opportunity costs of conservation, but where the strategic investment of expertise and conservation funds could meet multiple goals simultaneously. If our results are representative of other places, lands for water provision and flood control may be particularly important for biodiversity conservation (Table 2).


    The inclusion of ecosystem services in conservation planning has great potential to provide opportunities for biodiversity protection. This preliminary exercise seems to suggests that conservation planning for other services—either separately or in combinations with biodiversity—may result in considerable declines in the ability to meet biodiversity conservation targets, but this finding stems from assuming no new opportunities. Furthermore, strategic choices of particular services to include in conservation planning can yield considerable gains. Our strategic network of five benefits—biodiversity, carbon, flood control, recreation, and water provision—eliminated negative associations between features. This “Strategic” network met targets far better than did the “All” benefits network, both overall and especially for biodiversity protection (Figure 5).

    This study suggests that planning for ecosystem services would involve a major shift toward new geographies and a broadening of current conservation goals. The potential payoffs of such a shift are tremendous for both biodiversity conservation and human well-being [2,33,95,96], promising to sustain critical services, open new revenue streams, and make conservation broad based and commonplace. The goal of simultaneously maximizing biodiversity conservation and ecosystem services critical to poverty alleviation and general human well-being is one that can be embraced by all.

    Lab 11 Animal Behavior

    Ethology is the study of animal behavior. This involves observing an organism’s behaviors, interpreting what is observed, and research different organisms. Ethologists study and observe an organism’s reaction to the environment around them.
    Biotic and abiotic factors are limiting factors that control the maximum size of a given population. Favorable conditions are desired by an organism of its home environment. Because of this, an animal must search for the environment to fit its structure and lifestyle. This is called habitat selection.
    An animal can display many different types of behaviors, two being taxis and kinesis. Taxis behaviors are deliberate movements toward or away from a stimulus. Kinesis is a random movement that is not oriented toward or away from a stimulus. Taxis behaviors are exemplary of the physiological needs of an organism. Other behaviors are agonistic, aggressive or submissive actions toward another organism or mating behaviors.

    To observe animal behavior in this lab, isopods will be isolated in a controlled environment. Isopods, more commonly known as pill bugs, are crustaceans with a hard exoskeleton, seven pairs of legs, and antenna.
    Drosophila embryos develop in the egg membrane. Once the egg hatches, the new larva emerges and feeds on the medium. The larval stage has three instar stages. After undergoing all these stages, the larva molts and becomes a pupa. The pupa pupates and emerges as an adult fly. A fly reaches adulthood about two weeks after hatching, and lives as an adult for only two weeks.

    The following materials will be needed to complete the experiment: 10 pill bugs, hydrochloric acid solution 1%, potassium hydroxide solution 2%, 1 animal behavior tray, 2 pieces of filter paper, 1 camel’s hair brush, 1 magnifier or a dissecting microscope, and masking tape.

    a) First, place the 10 pill bugs into the animal behavior tray and take general observations of their movement and interactions for at least 10 minutes. Make a list of the data you have collected. Sketch a drawing of the dorsal and ventral surfaces of an isopod and label any recognizable structures.
    b) Now, label one side of the behavior tray A, and the other B. Place five pill bugs on each side and for 10 minutes record how many are on each side of the tray. Make observations each minute for 10 minutes. Record this data in a table. Calculate the average number on each side of the tray during the 10-minute period. The taxis behaviors have just been recorded.
    c) Next observe pill bugs in an altered environment. Using a chemotaxis, the behavior of the organism should differ from the last part of the experiment. A chemotaxis is the orientation of an organism in relation to the presence of a particular chemical. Remove the pill bugs from side B of the tray. Lay a piece of filter paper moistened with 1% hydrochloric acid solution over the surface of the tray.
    d)Now allow the pill bugs to roam freely through the tray and observe their behavior. Starting with five pill bugs on each side of the tray, make note of the number of pill bugs on each side of the tray every minute for five minutes. Make a table to show the data collected.

    a) General Observations
    -The larger pill bugs climbed over the smaller sized ones.
    -Movement mostly around edges of tray.
    -Legs move in quick, fluid motion.
    -Use antenna to sense closeness of other isopods.
    -When flipped on back side, will kick off from ground or other pill bugs to flip back over.
    -Seven pairs of legs.

    Table 1 Pill Bug Taxis

    Number of Pill Bugs in Side A Number of Pill Bugs in Side B Observations
    1 8 2 All moved to one side, then to the other
    2 5 5 Climbing on top of one another
    3 4 6 Some not moving five in one pile along the edge
    4 1 9 Most moved to side B, then less movement
    5 4 6 Little movement then became more active
    6 2 8 Still mainly on side B
    7 8 2 Many moving slower
    8 8 2 Separated out, less grouping
    9 6 4 Slower moving
    10 7 3 Large groups sitting with no movement
    Average 5.3 4.7

    b) Experiment Information
    Hypothesis: When a piece of filter paper moistened with a weak acid to one of the sections of the tray, the pill bugs will move into the acid-free area and stay away from the acid.

    Design: A small amount of a weak acid will be dropped onto filter paper and placed on one side of the behavior tray. Five pill bugs will be put on each side and reading on the number of pill bugs on each side will be taken every minute for 10 minutes.

    Data: Table 2
    Pill Bug Taxis with Altered Environmental Condition

    Number of pill bugs on side A Number of poll bugs on side B (side with acid) Observations
    1 7 3 Some backed away when they sensed the acid
    2 4 6 More mobile on side with acid (side B)
    3 3 7 Ones on side A not as active not moving much
    4 2 8 Less movement not very active
    5 4 6 Little movement on side B
    6 1 9 Sitting against wall in group
    7 2 8 Not very active on side B
    8 0 10 Most not moving
    9 0 10 Only one active
    10 0 10 Barely any movement

    1) Based on class data, what environmental conditions do pill bugs prefer?
    The pill bugs seemed to like the acid and stayed in the area where the acid was concentrated.

    2) How do you think pill bugs sense these conditions?
    Isopods used their antenna in sensing these kinds of alterations in the environment.

    3) Are there any results from Part 1 of the lab that would justify your hypothesis?
    Pillbugs did not seem to be attracted to acid.

    5) Would you find pill bugs in each of the following environments?
    a) In a pine forest under a log – yes, pill bugs are attracted to acid
    b) In a garden in northeast U.S. under a pile of straw – yes, pill bugs like dark places
    c) Under a house in an arid part of Arizona – no, pill bugs like moist environments

    Error Analysis:
    No apparent mistakes were made.

    From this experiment, the behaviors of isopods were observed and investigated. Through the acid test, it was found that the pill bugs found the acid desirable and stayed in areas with it. This proved the hypothesis that was stated was incorrect.

    Watch the video: BIOS1910 Lab#8 EyeModels (December 2022).