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Spatial sequence of autumn leave colouring: top to bottom

Spatial sequence of autumn leave colouring: top to bottom



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Leave colours on trees seem to vary on the same tree from top to bottom. Leaves higher up are already further ahead in the shedding process. What's the reason for this tempero-spatial gradient?


Spatial sequence of autumn leave colouring: top to bottom - Biology

All living things are composed of cells. This is one of the tenets of the Cell Theory, a basic theory of biology. This remarkable fact was first discovered some 300 years ago and continues to be a source of wonder and research today. Cell biology is an extremely active area of study and helps us answer such fundamental questions as how organisms function. Through an understanding of how cells function we can discover how human ailments, such as cancer and AIDS, can be possibly treated.

The Cell Theory

  1. All life is composed of cells
  2. Cells are the fundamental units which possess all the characteristics of living things
  3. New cells can only come into existence by the division of previously existing cells

Notice that this scientific concept about life is called a theory. In science, unlike the layman’s definition, the word theory is used for a hypothesis about which there is a large body of convincing evidence. Under experimental conditions all observations have thus far confirmed the theory. The evidence that helped formulate the theory was obtained using the microscope. The microscope is of enormous importance to biology and has extended our ability to see beyond the scope of the naked eye.

When we look at cells under the microscope, our usual measurements fail to work. In science, the metric system is used to measure objects and, as you will see, is vastly superior to our antiquated English system of measurement. Here are the basic units:

Length Volume Weight
1 meter (m) 1 liter (L) 1 gram (g)
1 millimeter (mm) = 0.001 m or 10−3 m or 1/1,000 m 1 milliliter (ml) = 0.001 L or 10−3 L 1 milligram (mg) = 0.001 g or 10−3 g
1 micrometer (mm)= 0.000001 m or 10−6 m or 1/1,000,000 m 1 microliter (ml) = 0.000001 L or 10−6 L 1 microgram (mg) = 0.000001 g or 10−6 g
1 nanometer (nm)= 0.000000001 m or 10−9 m or 1/1,000,000,000 m

There is also a different scale for temperature: Celcius.

  • 100˚ Celcius (C) = water boiling (equivalent to 212˚ F)
  • 0˚ C = water freezing (equivalent to 32˚ F)

Converting between units can be confusing. The most effective way to do this is by using conversion factors and canceling units. For example, if you want to know how many liters are in 425 milliliters, you can set up a simple equation that looks like this.

Practice

1.2 mm = ________ mm 0.224 m = ________ mm 225 nm =___________mm
0.023 L = ________ ml 750 ml = _________L 50 ml =___________ L

Background

Plants can make profound changes to their developmental and metabolic processes in response to changes in their environment. These adaptations require changes in the expression of many genes, and understanding these changes is of interest for both pure and applied sciences. Following the introduction of DNA microarray technology these massive changes in gene regulation can be assayed in a high-throughput manner. However, the production of microarrays requires the genome of the target organism to have been subjected to large-scale sequencing and, to date, microarrays have only been available for a limited number of plants, including the model plant Arabidopsis and the annual crops rice, wheat, Medicago, tomato and maize [1]. These species are not useful for a number of biological questions, because they do not form wood and do not undergo perennial growth and dormancy. We have now developed genomic tools to allow the genus Populus (aspens and cottonwoods) to be a woody perennial model for investigating fundamental aspects of tree biology. Towards this end we have undertaken large-scale EST sequencing programs [2, 3] and created wood-specific microarrays to construct a transcriptional roadmap of wood formation [4]. Here we describe the production of an extended Populus microarray and its application to study one of the most important aspects of plant biology that cannot be studied in annual plants: the induction of autumn leaf senescence.

Autumn leaf senescence is a developmental process that is poorly understood at the level of gene expression. All previous molecular studies on leaf senescence have focused on annual plants, where senescence could be induced by various treatments such as drought, oxidative stress, mechanical stress, shading or simply aging of the leaf. Autumn senescence in trees in temperate regions is typically induced by the shortening of the photoperiod, an environmental signal that also induces growth cessation and bud set. Autumn leaf senescence in Populus, shares many features with leaf senescence in annual plants [3] however, the induction of autumn senescence remained to be studied. Like other photoperiod-controlled processes, such as flowering in many plants, the initial perception of the critical change in daylength is phytochrome mediated [5], but other environmental cues, in particular low temperature, also influence the process. Virtually nothing is known about the signal transduction mechanisms from daylength perception to autumn leaf senescence in aspen and other trees. In northern Sweden, growth cessation and bud set occur about a month earlier than visible leaf senescence. It is not known whether separate critical night lengths trigger the two processes, or if the leaf senescence program takes much longer to orchestrate. We have addressed these questions here, by studying the pattern of gene expression during autumn senescence in leaves of a field-grown aspen tree, using DNA microarrays.


Plant Biology - Leaf Structure

The leaf is the site of two major processes: gas exchange and light capture, which lead to photosynthesis.

If you’ve ever eaten a piece of lettuce, cabbage, celery or onion, you’ve eaten a leaf or at least part of it. Celery is a petiole, which is the part of the leaf that connects the blade to the stem. Now that you’re acquainted with what leaves look and taste like, let’s take a closer look at what’s on the inside of leaves.

The leaf is arranged like a layered cake. It has a spongy layer, a palisade layer that consists of parenchyma cells and is where photosynthesis happens and an outer layer (the epidermis) with little holes (stomata) that could hold birthday candles. The only problem with our lovely analogy is that the candles would have to go on the bottom of the cake, since most of the stomata are on the bottom of the leaf. We’ll come back to that.

The inside cake layers are made up of parenchyma cells. This tissue is called the mesophyll, meaning "middle leaf," and comes in two flavors: the palisade mesophyll (sometimes called palisade parenchyma) and the spongy mesophyll. Most photosynthesis takes place in the palisade mesophyll, which is conveniently located at the top of the leaf just under the epidermis. Palisade parenchyma cells are long, bunched close together, and look like sausages hanging from the ceiling in a butcher’s shop.

The inside of the leaf looks like this:

Spongy mesophyll cells are not packed so tightly together, which allows carbon dioxide and oxygen to reach the palisade cells where they are needed in photosynthesis. Spongy mesophyll cells and guard cells (see below) also get some photosynthetic action.

Carbon dioxide and oxygen can’t just diffuse across the epidermis to get into the leaf. They have to be let in through special doors called stomata. Stomata can be on both sides of the leaf, but are usually concentrated on the bottom of the leaf to limit water loss due to evaporation.

If you collect leaves from many different plants, you’ll notice that they don’t all look the same. They have different shapes, different venation (the way the veins are arranged in the leaf), and are even attached to the stem differently. Often the shape of a leaf is advantageous in its environment, possibly because it doesn’t break off in strong winds or because it can limit water loss.

There are lots of kinds of leaf shapes, but one distinction is the simple vs. compound leaf. A simple leaf is, as its name suggests, pretty simple. It’s just a blade connected to a stem by a petiole. Oaks and maples have simple leaves, as do beeches:

Compound leaves look like a bunch of leaves that all come from the same stem. However, this stem is really a petiole, and the individual blades connected to it are called leaflets ("little leaves").

An ash leaf is an example of a compound leaf:

In addition to simple and compound leaves, there are some obvious differences in leaf shape. Maple leaves don’t look the same as oak leaves or aspen leaves. Some leaves are lobed, like an oak leaf (on the left below) others are serrated with little teeth. Even compound leaves can take on different shapes. Palmate leaves look like the palm of a hand with fingers spread out, and pinnate leaves have their leaflets arranged on opposite sides of the stem.

Leaves on the same plant can have different shapes. This occurs if some of the leaves were produced by the juvenile plant and later leaves were produced when the plant matures. Leaf thickness and size also varies within a plant, depending on whether the leaf is in the sun most of the day (a "sun leaf") or in the shade most of the day (a "shade leaf"). Sun leaves are smaller and thicker because they pack a bunch of chloroplasts into a small space. Since shade leaves don’t get as much light, they tend to be bigger but thinner, with a wider surface area to capture what light they do receive.

The pattern of veins on a leaf is usually determined by whether the plant is a monocot or eudicot. Monocots have a few parallel veins running down their leaves, which is called parallel venation. Eudicots typically have networks of veins, which is called net venation.

The way leaves are arranged on a plant isn’t something you probably think about everyday, or even at all. However, just as pastry chefs pay attention to where every frosted detail goes when the Food Network has a cake challenge, plants care about where their leaves go. Sometimes leaves are very neatly arranged in an alternating pattern. Other plants try to optimize the light their leaves receive by spreading their leaves out around the stem, in an effort to limit the amount of shade they receive from their own leaves. The arrangement of a plant’s leaves on the stem is called phyllotaxy.
Though most leaves perform photosynthesis, some plants have modified their leaves to do other things. Cacti are a great example of this: those prickly spines are actually leaves. The green part of a cactus is actually a photosynthetic stem. By producing leaves that are thin and pointy, cacti reduce water loss, which could be quite substantial in a desert, and discourage animals from eating their precious leaves or stems. Onions are also modified leaves, but they store food for the plant. Other plants, including many desert succulents (e.g. Aloe vera) use their leaves as storage organs, keeping water in them. Pea plants have tendrils, which are vine-like extensions of leaves. Tendrils can cling to other plants or structures to support the pea plant. Other plants, such as poinsettas, have colored leaves to help attract pollinators.

Many people are familiar with the vibrant autumn leaf displays of the northern United States. Red, orange and yellow leaves fill up the woods and later, people’s lawns. Why do leaves get all dressed up in pretty colors just before they fall from the tree?

The autumn leaves you see are senescing—getting old. Most angiosperms in cold climates can’t keep their leaves through the winter there just isn’t enough sunshine or water to keep up their metabolism in the winter, so they salvage what nutrients they can before the leaves drop off the tree. The bright colors you see are the pigments that are left after the tree has reabsorbed what it can from its leaves.

Colored compounds called anthocyanins make reds, and other pigments called carotenoids appear yellow or orange. These pigments have been there all along, but are only visible once the plant stops photosynthesizing and stops making chlorophyll. In other climates where leaves don’t change color all at once, they still senescence and drop off the tree. Often old leaves turn yellow, but an individual plant may only have a few yellow leaves at any given time.

Just like curly-haired people sometimes straighten their hair and straight-haired people sometimes curl their hair, plants like to change their appearances too. Some plants can change the way they hold their leaves throughout the day to maximize the sunlight they receive. Lupines always have their leaves fanned out to receive direct sunlight. Other plants, particularly those that live near the equator and get more intense sunlight, change the orientation of their leaves to minimize the solar radiation they get. These plants line their leaves up so that the sun hits the leaf edge rather than the top part of the blade.

Brain Snack

Stone plants are plants whose leaves are mostly underground. Only the tops of the leaves are visible, and the leaves look like rocks:


DISCUSSION

The coloration of the leaves of deciduous trees in the fall in temperate regions is perhaps the most striking example of leaf senescence. Therefore, it is rather surprising that there are no data on gene expression during autumn leaf senescence. This type of leaf senescence is probably very similar to senescence in, for example, detached leaves, but there must also be differences. The autumnal leaf senescence program is induced in all the leaves by decreasing day length, regardless of whether they are stressed by other environmental factors. There is also a lot of natural variation in the regulation of the process. For instance, in an adaptation to the earlier autumns in the north, trees from higher latitudes start senescing earlier than trees from lower latitudes ( Pauley and Perry, 1954). We have embarked on a project to elucidate the genetic basis of autumn senescence in aspen leaves, and describe here the first steps and results. We have performed large-scale EST sequencing to isolate candidate SAGs and to study the differences in overall gene expression between senescing leaves and young, but fully expanded, leaves. As a reference material, we have chosen young, greenhouse-grown leaves to maximize gene finding. This is assuming the comparison will detect not only differences in gene expression dependent on senescence, but also differences induced by various environmental stresses imposed on leaves of the free-growing aspen. Further studies will reveal which of the genes discussed in this paper that are truly senescence associated and those that are induced by various stress treatments, but even at this stage we can draw several interesting conclusions about gene expression in autumn leaves.

Estimating expression levels from EST frequencies is an indirect method and there are both technical and biological limitations to such an analysis. For example, uneven efficiency in the reverse transcription of mRNAs of different sizes, size fractionation, and the possible recalcitrance of some genes toward cloning in Escherichia coli are all problems that may affect the results. Despite these limitations, the “digital northern” approach has a major advantage over traditional northern or most DNA chip array experiments because it gives data on mRNA levels for individual genes relative to the total mRNA pool. mRNA levels do not necessarily correspond well to protein synthesis, and there are many well-documented examples of translational regulation of gene expression in plants. However, for most major enzymatic components, EST abundance seems to be a fair approximation of relative protein abundance ( Jansson, 1999 Mekhedov et al., 2000 Ohlrogge and Benning, 2000). For all genes that we have tested so far, RNA blotting has given similar results to EST frequency analysis, although direct methods have to be used to obtain high-quality data for individual genes. We are now performing large-scale transcript profiling using DNA microarrays to get further information about the precise transcript abundance of the different genes.

The pattern of gene expression in the two libraries was strikingly different. Our data indicate that most of the metabolic characteristics previously reported for senescing leaves (down-regulation of photosynthesis and up-regulation of genes involved in protein, lipid, pigment degradation, and respiration, as well as stress-related genes for review, see Smart, 1994) were also found in aspen autumn leaves. For the majority of genes previously shown to be senescence associated in annual plants, we found the same in autumn leaves. This confirms that the general pattern of metabolism is the same in autumn leaves as in senescing leaves of annual plants. Some of the genes previously reported to be SAGs in other systems were not well represented in the autumn leaf library. The transcript profiling that we performed on a limited number of genes shows that mRNA for several of them accumulates later in the process, and it is likely that the majority of the known SAGs will also prove to be SAGs in the autumn leaf system. However, we also believe that we will be able to identify genes whose expression patterns in autumn leaves are not mimicked in senescing leaves of annuals such as Arabidopsis. Because the process is triggered differently we expect there to be at least some regulatory proteins that have a specific role in inducing autumn senescence.

In the young, greenhouse-grown leaves a very large proportion of the mRNA pool (and, thus, protein synthesis) was devoted to synthesis of the photosynthetic apparatus: 33% of the ESTs encoded proteins known to be components of the various protein complexes involved in photosynthesis. In contrast, only 5% of the clones in the library from senescing leaves encoded photosynthetic proteins and one-half of those were stress-related photosynthetic proteins such as ELIPs. The average gene encoding a “standard” photosynthetic protein, directly involved in light reaction or CO2 fixation, was down-regulated about 20-fold in the autumn leaves. We expected gene expression in young leaves grown under non-stressed conditions to be heavily concentrated on photosynthesis, but we were surprised to find how little of the gene expression in autumn leaves, which still showed no visible sign of chlorophyll degradation, was dedicated to photosynthesis. Senescence is a strictly controlled developmental process and by the middle of September, photosynthetic gene expression had apparently been turned off and the leaves had prepared to break down their chloroplasts.

In addition to the confirmation that autumn senescence shares many features with senescence in leaves of annual plants, we also identified a number of genes whose orthologs in Arabidopsis are either unknown or have not been connected with senescence. By choosing leaves in the process of chlorophyll degradation as sources for the autumn leaf cDNA library, we believed that we could get a snapshot of the protein synthesis activity related to degradation of the chloroplasts, and possibly other cell constituents as well. Of our identified 35 Paul genes, nine encoded proteins Arabidopsis orthologs seem to be chloroplast located, and four of these have no assigned function. These are all good candidates for proteins involved in degradation of the chloroplast components, and we also found two known chloroplast proteases, DegP1 and FtsH2 ( Adam et al., 2001), and one enzyme involved in chlorophyll degradation among the genes apparently up-regulated in the autumn leaves.

Another striking difference was the higher fraction of ESTs in the autumn leaf library that showed no significant homology to any known protein in public databases. This could simply be a consequence of the fact that young, green leaves have been very extensively studied and the proteins of such leaves are better characterized. Because gene prediction also relies on EST data, genes expressed in tissues that not have been subjected to EST sequencing are overrepresented among genes for which no orthologs have been found, and/or whose putative function remains unknown. This means that we may overestimate the fraction of “truly novel” genes in our data but, even so, there are many potentially interesting genes to be found in autumn leaves of aspen.

A prominent feature of the nuclear genome of plants is the large fraction of genes that appear to have originated from the cyanobacterial genome. It is believed that in the evolution of the green plant, a cyanobacterial progenitor of the chloroplast was engulfed by the eukaryotic host, becoming enclosed by a double membrane, and then permanently integrated into the plant cell as an organelle, the chloroplast. The ancestral chloroplast genome has been estimated to have consisted of around 3,200 genes, roughly 1,700 of which have been lost because of redundancy between the nuclear and plastid gene products, and about 1,400 genes appear to have been transferred to the nuclear genome, leaving only 87 plastid-encoded genes ( Sato et al., 1999 Abdallah et al., 2000). EST sequencing does not give direct information concerning organelle mRNA content, but our data can also be used to indirectly estimate organelle protein synthesis in aspen leaves. The basis of the calculation is that nearly all of the proteins encoded in the organelle genomes form, together with nuclear-encoded subunits, multiprotein complexes in which the protein subunits and their relative stoichiometries are known in great detail. Because we can calculate the average EST frequency of all nuclear-encoded subunits of these complexes and assume that the plastid-encoded subunits are synthesized in matching amounts, we can estimate relative rates of chloroplast protein synthesis (details of these calculations are given in the supplemental material). Based on these calculations, we estimate plastid protein synthesis to account for 130/5,128 ≈ 2.5% of the cytoplasmic protein synthesis in autumn leaves and 1,144/4,842 ≈ 24% in young leaves. Although these figures are only indirect estimations and may not be quantitatively accurate, they strongly indicate that there is a massive down-regulation of plastid protein synthesis before any visible sign of autumn senescence.

We found no evidence for a conversion of peroxisomes to glyoxysomes, like in senescing rape (Brassica napus) leaves ( Vicentini and Matile, 1993) during this stage of senescence the key enzymes of the glyoxylate cycle (malate synthase and isocitrate lyase) and of gluconeogenesis (phosphoenolpyruvate carboxykinase and pyruvate orthophosphate dikinase) were not found among the ESTs from autumn leaves. However, the changes in subclasses of “energy” indicate that respiration and mitochondrial energy conversion were important in autumn leaves and that mitochondria have already taken over the chloroplasts' role as energy-generating organelles even before massive chloroplast breakdown has occurred. Formate dehydrogenase (one of the Paul genes) was strongly induced in the autumn leaves and can be involved in energy production in mitochondria without the participation of the tricarboxylic acid cycle. Alternatively, this enzyme may be involved in metabolism of C1 compounds, although the source of these putative metabolites cannot be defined at present. The senescing leaves are still source leaves because the phloem presumably transports recycled nutrients away from the leaves into the overwintering parts of the tree. The exact nature of the compounds transported is unknown, but amino acids with high nitrogen content (e.g. Gln and Asn) are obvious candidates. An important challenge for further research will be to define the metabolic pathways involved in nutrient recapture from senescing leaves.

Our data indicate that Cys and aspartic proteases may play an important role during chloroplast degradation, whereas at least the ubiquitin system (as evident from the RNA blot data) is not up-regulated until a later stage of senescence. It has been shown in other systems that the proteasome components do not accumulate during senescence ( Bahrami and Gray, 1999), but the enzymes of the ubiquitin pathway do ( Belknap and Garbarino, 1996). It is possible that the proteasomes present are sufficient to degrade the ubiquitinated proteins that, presumably, accumulate during later stages of autumn senescence. The senescence-associated Cys proteases may all be located in the vacuole or endoplasmic reticulum bodies ( Hayashi et al., 2001). This would be consistent with the secretory system having an important role in the senescence process. One of the Cys proteases we detected seems to be the aspen ortholog to SAG12, which has very restricted expression in Arabidopsis and Brassica napus, limited to the last stages of senescence ( Noh and Amasino, 1999b). The expression pattern in aspen was different, with one peak at the beginning of September, and one at the time when chlorophyll degradation started. The first peak correlated with weather factors likely to cause photooxidative stress, which would induce genes (PsbS and ELIP) that are up-regulated during light stress in aspen leaves (K. Wissel and S. Jansson, unpublished data). Because leaf senescence is probably an oxidative process ( Munne-Bosch and Alegre, 2002) unfavorable conditions may trigger degradative processes that, at least in part, may be reversed if weather conditions become more favorable again.

We believe that this work, in addition to discovering genes, provides insights into gene expression in aspen leaves at a rather early stage of autumn senescence. Moreover, it illustrates the usefulness of leaves of deciduous trees as a model system to study leaf senescence, and we believe that our ongoing transcript profiling using DNA microarrays will make it possible to pinpoint a number of candidate genes for regulators of the process. Ability to control senescence will have important biotechnological implications because trees that shed their leaves too early have lower than optimal productivity, whereas if the senescence process is initiated too late, the tree does not have sufficient time to recapture nutrients and complete the hardening procedure before the winter, and, thus, is likely to suffer from growth limitations and/or frost injuries. Therefore, this study (the first, to the best of our knowledge, in which gene expression during autumn leaf senescence has been studied) may be the first step to a deeper understanding of this biologically important process.


Why trees shed their leaves

November 2017 on Maple Street in Johnson City, Tennessee. Image via Teri Butler Dosher.

In temperate forests across the Northern Hemisphere, trees shed their leaves during autumn as cold weather approaches. In tropical and subtropical forests, trees shed their leaves at the onset of the dry season. Many types of trees shed their leaves as a strategy to survive harsh weather conditions. Trees that lose all of their leaves for part of the year are known as deciduous trees. Those that don’t are called evergreen trees.

Common deciduous trees in the Northern Hemisphere include several species of ash, aspen, beech, birch, cherry, elm, hickory, hornbeam, maple, oak, poplar and willow. In tropical and subtropical regions, deciduous trees include several species of acacia, baobab, roble, ceiba, chaca and guanacaste.

Image via Tosca Yemoh Zanon in London Photo via Daniel de Leeuw Photography

Most deciduous trees have broad leaves that are susceptible to being damaged during cold or dry weather. In contrast, most evergreen trees either live in warm, wet climates or they have weather-resistant needles for leaves. However, there are exceptions in nature, such as tamarack trees that shed their needles every autumn and live oaks that retain their broad leaves for the entire year even in relatively cool climates.

Shedding leaves helps trees to conserve water and energy. As unfavorable weather approaches, hormones in the trees trigger the process of abscission whereby the leaves are actively cut-off of the tree by specialized cells. The word abscission shares the same Latin root word as that in scissors, scindere, which means “to cut.” At the start of the abscission process, trees reabsorb valuable nutrients from their leaves and store them for later use in their roots. Chlorophyll, the pigment that gives leaves their green color, is one of the first molecules to be broken down for its nutrients. This is one of the reasons why trees turn red, orange, and gold colors during the fall. At the end of the abscission process, when the leaves have been shed, a protective layer of cells grows over the exposed area.

Layer of abscission cells separating a leaf from its stem. Image Credit: U.S. Forest Service.

The shedding of leaves may also help trees to pollinate come springtime. Without leaves to get in the way, wind-blown pollen can travel longer distances and reach more trees.

Autumn leaves. Image Credit: Tracy Ducasse.

Bottom line: Many types of trees shed their leaves as a strategy to survive cold or dry weather.


How to Preserve Leaves: Wax Paper Pressing Method

One of the most common ways to preserve leaves is by pressing them between wax paper.

What You Need:

What You Do:

  1. Place a leaf between two pieces of wax paper.
  2. Put a towel or a piece of thick paper over the wax paper.
  3. Press on the towel or paper with a warm iron to seal the wax sheets together. This takes about 2-5 minutes on each side, depending on how moist the leaf is. Once you have finished one side, flip the leaf over and do the other side.
  4. Cut around the leaf, leaving a small margin of wax paper to ensure that it will stay sealed.
  5. Rather than cutting out the leaves, you may want to try to peel the wax paper off the leaves, leaving a coat of wax behind to protect the leaves. Try this on one leaf first to see if this method works for you.

INTRODUCTION

A bacterial biofilm can be defined as a multicellular community attached to a surface and embedded in a matrix of extracellular material (1). The advantages of growing in biofilms are many. By growing attached to a surface, bacteria can stay indefinitely in a favorable environment under conditions where external forces, such as flowing water, would otherwise sweep them away. In biofilm communities, bacteria are more resistant to antibiotics (2) and may also avoid predation through microcolony formation (3). Microbial biofilms in general often contain intricate structures, such as channels and voids (4), affecting important processes, including nutrient delivery to the populations present (5). Biofilms containing multiple functional groups competing for resources or collaborating under mutualistic forms are therefore exceptionally complex, and even more so if each functional group contains several populations, each occupying its own ecological niche. The positions and spatial relationships of cells within biofilms or similar environments reveal information about important interactions. Different examples where such spatial information is important include oral biofilms involved in periodontal diseases (6), biofilms in streams (7), bacterial communities on leaves and root hairs (8), syntrophic propionate-oxidizing cells and methanogens in upflow anaerobic sludge blanket granules (9), methanogenic-sulfidogenic aggregates (10), and aerobic ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB), which often grow in intricate heterogeneous multispecies biofilms (11�).

Current techniques, such as confocal laser scanning microscopy (CLSM), allow the systematic collection of high-quality biofilm images suitable for digital image analysis. Especially when combined with specific molecular labeling methods, most notably fluorescence in situ hybridization (FISH) with rRNA-targeted probes (16), image analysis offers great possibilities for the use of spatial statistics and stereology for quantifying the spatial arrangement of microbial populations (8, 17, 18). Some of these approaches have already been implemented in computer software (8, 17). Nevertheless, certain research questions are still difficult to tackle. For instance, the directionally dependent (anisotropic) structure of stratified biofilms may cause biases when such samples are analyzed by stereological methods that assume isotropy (19). Stratification likely reflects the occurrence of important environmental or biological factors affecting biofilm growth, such as substrate concentration gradients. Therefore, adequate tools to analyze the distribution of the organisms in such biofilms would be highly useful.

Here, we developed improved methods for quantifying the vertical distribution and the coaggregation patterns of defined biofilm populations. Their use is demonstrated by analyses of stratified nitrifying biofilms inhabited by AOB from the phylogenetic Nitrosomonas oligotropha cluster 6a and NOB from the genus Nitrospira. Members of these lineages are frequently encountered in nitrifying biofilms in wastewater treatment plants (WWTP) and play major roles in the removal of nitrogen from sewage (12, 20�). Previous investigations have demonstrated the formation of substrate gradients in such biofilms, which lead to pronounced stratification and distinct spatial distributions of AOB and NOB (12, 15, 24).

Different biofilms were obtained from a pilot size moving-bed biofilm reactor (MBBR), a pilot size nitrifying trickling filter (NTF) system, and a full-scale NTF of a domestic WWTP. A sequential FISH protocol was developed and applied to detect multiple nitrifying bacterial populations simultaneously and efficiently while preserving the possibility to quantify their abundance relative to all bacteria in the sample. This technique was combined with a new image analysis tool that automatically cuts biofilm images into sections that represent different depth zones, thus allowing the precise quantification of microorganisms in the layers of stratified biofilms. As AOB and NOB are partners in a mutualistic symbiosis (25), they frequently coaggregate in nitrifying biofilms and flocs (20, 21, 24). However, the overall strength of coaggregation and the preferred distances between the AOB and NOB microcolonies can vary among different phylogenetic clades (18). Therefore, the coaggregation patterns of the different nitrifiers were quantified by a recently developed algorithm that works with strongly anisotropic samples (19).


The sensory ecology of adaptive landscapes

In complex environments, behavioural plasticity depends on the ability of an animal to integrate numerous sensory stimuli. The multidimensionality of factors interacting to shape plastic behaviour means it is difficult for both organisms and researchers to predict what constitutes an adaptive response to a given set of conditions. Although researchers may be able to map the fitness pay-offs of different behavioural strategies in changing environments, there is no guarantee that the study species will be able to perceive these pay-offs. We thus risk a disconnect between our own predictions about adaptive behaviour and what is behaviourally achievable given the umwelt of the animal being studied. This may lead to erroneous conclusions about maladaptive behaviour in circumstances when the behaviour exhibited is the most adaptive possible given sensory limitations. With advances in the computational resources available to behavioural ecologists, we can now measure vast numbers of interactions among behaviours and environments to create adaptive behavioural surfaces. These surfaces have massive heuristic, predictive and analytical potential in understanding adaptive animal behaviour, but researchers using them are destined to fail if they ignore the sensory ecology of the species they study. Here, we advocate the continued use of these approaches while directly linking them to perceptual space to ensure that the topology of the generated adaptive landscape matches the perceptual reality of the animal it intends to study. Doing so will allow predictive models of animal behaviour to reflect the reality faced by the agents on adaptive surfaces, vastly improving our ability to determine what constitutes an adaptive response for the animal in question.

1. A complex problem

Understanding how plasticity in behaviour is generated through the interaction between genetic, epigenetic and environmental factors is a major research area in behavioural ecology [1–3]. Despite decades of research, characterizing the ways animals respond to their environments remains a challenge for behavioural ecologists. The incredible complexity of interactions among phenotypes, their development and the environment in which they reside means that accurately predicting how an animal will respond under a given set of conditions is only rarely possible and never straightforward. The limitations occur on two fronts: first, as researchers, we may be unable to measure or observe the internal and external aspects of the animal's environment that influence its behaviour [4]. Second, even if we are able to identify factors of the environment that are known to influence behaviour, then we need also to understand the umwelt of the animal—how it perceives the world around it as a function of its sensory abilities. The multidimensionality of potential interactions among factors that influence behaviour means that making predictions about the optimality, adaptive value and evolutionary trajectories of behavioural phenotypes is a formidable challenge, to say nothing of the difficulty in measuring how animals perceive these factors. Nevertheless, it is a challenge that must be met if we are ultimately to understand the evolution of behaviour. In this review, we discuss how modern analytical approaches may combine studies of optimality, sensory ecology and behavioural plasticity to meet this challenge.

2. Considering the umwelt of our study species

The field of sensory ecology has made great inroads into understanding how variation in the sensory abilities among individuals and species influences animal behaviour [5,6]. Although traditionally focused on the mechanistic bases of sensory biology [7–9], the field of sensory ecology increasingly incorporates evolutionary theory into analyses of sensory biology, examining the phylogenetic, developmental and allocation trade-offs inherent in sensory systems [4]. These considerations are especially pertinent when considering behavioural plasticity in response to variable environments. To maximize fitness across all contexts, the optimal behavioural strategy would of course be to perceive and respond to information about the environment with perfect accuracy. Yet, the perceptual space or umwelt of an animal—the world that it can access through its own sensory systems—may place bounds on its ability to adaptively respond to environmental stimuli. Similarly, in cases where an individual is able to perceive differences in relevant ecological factors, it may lack the behavioural plasticity required to effectively respond to these factors. Turning a blind eye (or deaf ear, or anosmic nose) to the ability of the individual to perceive and respond to its environment will lead to a disconnect between our predictions of adaptive animal behaviour and the sensory reality defined by the umwelt of our study species. Underlying sensory limitations may lead us to conclude that animals are making maladaptive transitions among behavioural states when they are actually acting optimally given the information they are able to collect from their environments. Alternatively, in some cases, organisms have access to perceptual space that is inaccessible to humans birds and bees [4,5] can see UV light [6], and bats can hear ultrasound [7], potentially clouding our ability to make reasonable predictions about adaptive animal behaviour based on our own experience.

A good example of the limitations sensory biology may place on behaviour is seen in the relationship between temperature, sex determination and adaptive sex allocation. For some fish [10] and many reptiles [11], sex is determined by the ambient temperature during early development (temperature-dependent sex determination, TSD). This can facilitate adaptively plastic allocation of offspring sex if brood sex ratio can be manipulated to favour the rarer sex. A recent study on painted turtles (Chrysemys picta) demonstrated that females choose nest sites with temperatures that will give rise to an adaptive offspring sex ratio [12]. This result may lead to predications about optimal sex allocation in other related taxa. Yet, most studies have failed to convincingly demonstrate that females choose nests that result in optimal sex ratios [12]. Indeed, the variation in environment that potentiates adaptive sex allocation may render populations susceptible to maladaptive sex ratios if these cannot be accurately perceived or predicted [13]. Although there is a clear benefit of sensing both the population sex ratio and the likely nest temperatures to optimally adjust sex ratios, there is a paucity of evidence that this actually occurs. Arguments about adaptive nest site choice based on temperature must therefore be tempered by the fact that it is far from clear that animals with TSD can always sense temperature or population sex ratio.

Sexual selection by mate choice is another, and an especially intriguing, domain where perceptual biology plays a significant role [14]. A controversy in sexual selection is the degree to which females base their mate choice on traits that are correlated with heritable variation for offspring survivorship [15]. Despite well-developed theory, the evidence for a ‘good genes' effect is sparse, and when it does occur, the effect is small [16]. The reasons for the paucity of evidence and effect, despite decades of research on good-genes mate choice, are varied. For example, there could be lack of heritable variation for survivorship, and direct benefits could be far more important than indirect benefits in driving the evolution of preferences [17]. Another possibility is that females might not always have perceptual access to underlying genetic variation, either because there is no phenotype–genotype correlation, or the correlation is so subtle that it cannot be perceived. This might be why the two most successful demonstrations of genetically based mate choice are associated with perceptual pathways known to be both accessible to choosers and indicative of genotype: choice for conspecifics versus heterospecifics [14], and choice for MHC compatibility [18]. In the former case, we know that there is strong linkage between the species-specific courtship traits and the genetic identity of the species even humans are able to identify species of many birds, frogs, crickets and fireflies by their courtship signals. In the latter, choice for compatible MHC genes appears to be based exclusively on odour cues [18,19]. There is no reason to think that all traits in all modalities will show the same statistical linkage with underlying genetic variation, and that this meaningful variation (i.e. the phenotype–genotype correlation) will be equally accessible in all modalities. Thus, arguments about optimal mate choice, even when based on theoretical ground as well traversed as good genes hypotheses, may break down when sensory ecology is ignored.

More generally, across modalities and taxa, we find examples of sensory limitations or errors leading to suboptimal or maladaptive behaviour [4]. In many bird species, photoperiod cues influence laying date, which may be adaptive if they align with later life-history events such as nesting time, but which can become maladaptive under the novel environmental conditions generated, for instance, by anthropogenic disturbance [20]. Similarly, for many invertebrates, anthropogenic sources of light can lead to navigation errors. Moths fly to the black-lights of entomologists as well as to urban lights [21], and mayflies lay their eggs on dry asphalt roads because they perceive the reflected polarized light as a water surface [22]. Perceptual limitations may also be directly exploited to manipulate the behaviour of con- and heterospecifics [14]. Male goodeid fishes have yellow bands that mimic worms, attracting females in search of prey, and male characin swordtails have a lure on the pectoral spine that resembles an ant water mites drum the water's surface to mimic the vibrations of their copepod prey, and moths mimic the echolocation calls of predatory bats to freeze their females in fright in order to gain sexual access (reviewed in [14]). Even in systems as seemingly robust as kin recognition in eusocial insects, perceptual limitations can lead to maladaptive responses. Workers of the killer bee Apis mellifera scutellata mistake the pheromonal bouquet of the Cape honeybee A. mellifera capensis for that of their own queen, rearing the larvae of these unrelated individuals and reducing their inclusive fitness to zero. In these cases, a cue has the potential to lead to an adaptive response in a plastic behavioural trait, but incorrect perception of the cue leads to maladaptive behaviour [23].

3. Adaptive behaviour on ‘powerfully seductive’ landscapes

How then can we incorporate the nuance generated by variation among individuals and species in their ability to even detect the parameters of our models? Although the complexity of relationships among factors influencing behaviour means that simple linear predictions become impossible, more sophisticated approaches to understanding adaptive behaviour in a complex world may be applied. Faced with comparable complexity in examinations of gene frequencies or phenotypes across generations, population geneticists and evolutionary biologists map fitness outcomes in multidimensional space using approaches based on genotype (e.g. adaptive landscapes [24]) or phenotype space (e.g. Pareto fronts [25]). As visual and heuristic models of evolutionary processes, fitness landscapes can be ‘powerfully seductive’ [26] owing to the appeal and intuitive understanding of terms such as peaks, ridges and valleys to describe fitness consequences of different allele frequencies or gene-by-environment interactions. These visual models provide us insights into the qualitative directions in which selection may push populations or phenotypes. Applying equivalent approaches to the study of animal behaviour holds great potential to predict or assess ‘adaptive’ behaviour, as they allow us to make predictions across a range of contexts, extrapolating into areas of unmeasured space, for example in the form of nutrient spaces [27] or landscapes of fear [28,29]. Moreover, the topology of the behavioural pay-off surfaces can be manipulated to reflect the sensory ecology of the animal being studied, potentially by directly combining perceptual space with adaptive space.

A major point to consider is how the original implementation of fitness surfaces and adaptive landscapes differs from any application of surfaces in behavioural studies. The fundamental difference comes in the perceptual capacity of the agents (alleles versus behaving organisms) on these surfaces. On Wrightian adaptive landscapes, evolutionary processes such as natural selection, drift and migration may change allele frequencies and cause populations to move through fitness peaks and troughs on the landscape [30], yet the process is blind because populations at any position on the surface cannot shift to maxima through any volitional process. Movement on these landscapes therefore represents the ultimate case of perceptual limitation—alleles are more or less frequent in the next generation, but themselves are inviolate ‘billiard balls' knocked about by selection [31]. Contrast this with behaving animals in fluctuating environmental conditions, which have the ability to perceive, to a greater or lesser extent [28], the pay-offs of different behavioural strategies. In this model, landscapes can be considered visual matrices where peaks and troughs represent direct/immediate (as opposed to indirect/generational) pay-offs as fitness proxies for reproductive success or energetic gains. The individual agents on these surfaces therefore have the potential to move about in real time, shifting directly to pay-off optima without moving through troughs of low fitness. Male spiders choosing among female mates, for example, respond to changing competitive contexts by rapidly switching their choices to increase the reproductive pay-offs under the new social conditions [32]. This capacity hinges on the individual's ability to perceive and assess the pay-offs of a particular behaviour in a given context, which can only be assessed with a detailed understanding of the perceptual and cognitive abilities of the animal being studied. Studies into behavioural optimality must therefore also assess the capacity of the behaving agent to distinguish among local and global optima. While this is a mathematically straightforward exercise, it may be beyond the abilities of study organisms in complex environments. Any study into the optimality of behaviour across environmental contexts therefore naturally dovetails with an assessment of perceptual and cognitive biology [33], but novel approaches may be required to successfully join these fields.

4. Successful use of surfaces in behavioural ecology

Pay-off surfaces have already been used successfully in behavioural ecology, for example, in the implementation of nutrient spaces to predict adaptive foraging behaviour [27]. On these surfaces, the axes represent the amount of protein and carbohydrate, respectively, in sources of food. The animals used in these studies have ‘protein targets', amounts of protein that they must reach in order to be satiated, and by plotting the ratio of protein to carbohydrate in nutrient space, testable predictions can be made about the foraging strategy animals will use. The high coherence between predictions and observed behaviour is a strong indication that the foraging animals are able to perceive the differing amounts of protein and carbohydrate in their food sources, i.e. they are able to perceive the surface on which their behaviour is being plotted. This may not always be a reasonable assumption, as we now outline using a very similar foraging problem based on food colour.

Many animals feed on food sources that differ in colour, and the colour of food sources represents some aspect of their nutritional value (e.g. Sylvia warblers and fruit colour [34]). When foraging animals need to balance their intake of different nutrients, it is intuitive and informative to use landscapes to visualize the fitness outcomes of differing foraging strategies [27]. As autumnal leaves senesce, they lose up to 70% of their nitrogen [35,36], which then decreases their nutritional value to herbivores. A visually spectacular correlate of this senescence (for the human umwelt at least) is a colour change from green to yellow to red, allowing us to create a hypothetical landscape with axes representing the nutritional pay-offs of probabilistic foraging on leaves of various colours. From this landscape, we might predict the adaptive optima for foraging on leaves of differing colours (figure 1a). But this exercise would ignore a caution long ago raised by Lord Rayleigh: the assumption that the world we can see and measure as scientists is in any way similar to that perceived by the animals we study ‘is a good deal to take for granted’ [37]. In fact, the evolutionary basis for this colour transition and the consequences for herbivores have been hotly debated since Hamilton & Brown [38] suggested that autumnal coloration is a case of aposematic coloration, deterring aphids from feeding on leaves in autumn (see also [39,40]). This claim led to the essential question posed by Chittka & Doring [35]: can aphids perceive these allegedly aposematic ‘signals', keeping in mind that no herbivorous insect studied to date has red colour receptors [36]? These researchers demonstrated that the yellow coloration of leaves actually acts as a super-normal stimulus because, to the visual receptors of aphids, yellow appears ‘very’ green and is more attractive to most species of aphids than green, whereas red colours appear dull to the aphid [35]. If we construct a landscape based on increasing toxicity or reduced nutrient value, and hence lower fitness of feeding on yellow or red leaves, we might generate figure 1a in which fitness peaks occur when a higher proportion of green leaves are eaten. On observing that aphids feed more readily on yellow leaves, and therefore sit lower on the fitness landscape, we may assume that the aphids currently reside on a suboptimal point on the landscape, and that selection would drive behaviour to feed primarily on green leaves. But, of course, to an aphid, a yellow leaf looks greener than a green one! As such, the fitness ‘optima’ perceived by the organism (figure 1b) is different from that perceived by the researcher. An adaptive behavioural response is therefore out of reach, because there is a fundamental disconnect between what we can visualize on a foraging fitness landscape and the biological reality of the surface that can be perceived by the organism.

Figure 1. A visual nutrient space of autumn leaves. The colour of autumn leaves has been suggested to signal either their toxicity (aposematism) or nutrient value to herbivores (e.g. green leaves are higher in nitrogen than yellow or red leaves). (a) Feeding on greener leaves with a higher probability (y-axis) than yellow or red leaves (x-axis) therefore leads to higher fitness pay-offs. (b) To the visual system of aphids, however, yellow leaves appear as a super-normal green stimulus, and will potentially be fed on with greater probability than other leaves. The fitness surface according to the aphid (b) is therefore very different from the actual fitness surface (a). (Online version in colour.)

Signals or cues that are employed to intentionally deceive the receiver highlight a more widespread phenomenon in which one individual's adaptive landscape is masked to benefit another individual. While the previously discussed limitation may be classified simply as an error, this category is better described as forced errors. In the context of heterospecific interactions, most predator–prey interactions are based on imperfect perception of the landscape in the sense that prey that could perfectly assess predation risk may never be eaten. This relationship between foraging and predation risk has been usefully explored in landscapes representing the ‘ecology of fear’ [28,29], again an adaptation of the Wrightian landscape. In these landscapes, resource matching interacts with predation risk to modify the spatial distribution and quitting times of foragers. When habitats vary in both their productivity and risk of predation, individuals are predicted to distribute themselves in a manner that maximizes the ratio of reward to risk [28,29]. Yet, for the predators, any behaviour that causes foragers to underestimate the risk of a particular patch will increase the chances of a successful predation event. Thus, there are a number of sensory pathways that prey may use to detect predation risk, and we illustrate how an animal's perceptual abilities influence its perception of such a landscape in figure 2. Studies have shown that the presence of predators can influence the behaviour of potential prey within the predators' range, such as restricting foraging activities [28,29]. Figure 2a illustrates the distribution of a predator species across space. In this example, there are three prey species that perceive the presence of the predator, each through one of three different sensory modalities, vision (figure 2b), sound (figure 2c) and smell (figure 2d). These three modalities give very different impressions of where predators are in terms of accuracy of present location, area over which prey are detected and time over which prey are detected. These perceptual differences could result in very different adjustments that each prey makes to activities that carry high predation risk, such as foraging and sexual displays. In particular, using any one of these surfaces to predict or assess adaptive responses to changes in predator distribution needs be done with respect to the relevant sensory pathways with which these predators are perceived.

Figure 2. (a) The distribution of a hypothetical predator across space. The presence of predators can create a ‘landscape of fear’ [8], altering the behaviour of prey. The landscape of fear, however, will vary with the ability of prey to perceive the presence of predators, but not all prey and not all sensory modalities sense predators in the same way. (b) The perceived presence of predators by prey that detect predators through the visual modality. Vision gives accurate information about the presence of a predator when it is seen. It relies on direct line of sight, so detection rate can be lower than in other modalities (the prey might not see all the predators in its visual field) and the size of the detection field is typically smaller than in other modalities, but the information about spatial location is more accurate. Note that compared with (a) the peaks in (b) are lower (fewer predators are detected than are actually present), the area of the base of the peaks is smaller (the visual field does not encompass the entire landscape), but the gradient is still steep (i.e. greater accuracy of information). (c) The perceived presence of predators by prey that detect predators through the auditory modality. Sound gives less accurate information about location than visual cues, but because a direct line of sight is not required predators can be detected by acoustic cues over a larger distance than visual cues. Sound is only emitted by the predator when it moves and when it voluntarily makes sound, such as in acoustic communication. Thus, compared with (b) the height of the peaks (predators detected) could be lower if predators tend to be silent, the areas around the peaks are larger (the acoustic detection field is larger than the visual detection field), and the peaks are less steep (the accuracy of localization information is lower). (d) The perceived presence of predators by prey that detect predators through the olfactory modality. Here, we assume that odours are deposited by a predator to mark its territory or home range and thus are non-volatile. Odours provide accurate information about where an individual was but almost no information as to where it is, and this information cannot be detected at any substantial distance from the odour source when odours are used for marking (unlike long-distance olfactory communication in moths and some other animals). Thus, compared with (c) the peaks are high (many prey are detected), the area over which prey can be detected is small and restricted to be within the actual range of the predator (unlike with visual and especially auditory cues when a predator can be detected from a prey outside of its range), and the peaks are not at all steep since the information about the where a predator is at the time the odour cue is sensed in not very accurate. (Online version in colour.)

5. Summary

As our knowledge of the complex interactions among phenotypes, environments and sensory ecology increases, behavioural ecologists increasingly require sophisticated tools to assess what constitutes an adaptive response. We have focused here on the clear heuristic and predictive power that behavioural fitness surfaces may provide, but also emphasize the caution that must be applied to ensure a meaningful link between the predictions we may generate from such models and the biological reality, or umwelt, of the taxon in question. With the advent of increased competition for readership from online and open access journals, many editors lean towards papers with increased visual impact, and computer generated multicoloured surfaces emphatically provide this. Nevertheless, without a proper grounding in the biological reality of the organisms being studied, these surfaces risk placing the cart-before-the-horse, providing predictions into space that cannot be accessed or distinguished by the organism being studied. A pressing question we must always ask in the application of fitness landscapes is whether the topology of the generated landscape matches the perceptual world of the animal it supposes to study. Here, we advocate overlaying traditional adaptive landscapes with the sensory reality of an animal's perceptual space such an approach could open new research avenues and provide powerful predictive tools of adaptive behaviour.

Let us close by saying that even when an organism has the sensory ability to assess the environment, the cognitive capacity to process this information and the behavioural plasticity to move to places of higher fitness pay-off, this may not occur. In any number of realms, humans are able to accurately predict the pay-offs of behaviours yet we fail to approach behavioural optima. From fisheries management to tobacco smoking, humans provide a wonderful illustration that even when an organism has near perfect information of the pay-off landscape (often by virtue of having created it), it cannot be assumed that adaptive peaks will be reached, or the ascent even attempted.


Watch the video: Every Watercolor Leaf Youll Ever Need AUTUMN EDITION (August 2022).