Questions to consider while studying for Exam #1

Note: this is not by any means a comprehensive study guide. Answers will not be posted. These questions are meant to put you in the mindset of the kinds of topics you will need to think and write about on Thursday.

The best way to study is to get together with a study partner, and ask each other questions!

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What are the force equations? What kind of forces does a stationary organism experience in a moving fluid? Are these forces constant? Do they vary with height/distance from the organism? How? What are some examples of organisms experiencing forces in fluids that we have discussed in class or in section? For instance, take a look at the Trussell paper. What are consequences of organisms living in high-flow areas?

Why is scaling important? What is allometric scaling? Give an example of allometric scaling from the literature. What is isometric scaling? Give an example from the literature. What is Kleiber's Law and how does it relate to scaling? Under what circumstances might this ratio change? What are the other variables that scale with body mass? (ex: population density, latitude, home range). Give a quantitative description of the Energy Equivalence Rule. This may help: http://repository.unm.edu/bitstream/handle/1928/6927/Damuth.pdf?sequence=1

Continuing with the theme of scaling, why might we look at ecological processes at different spatial scales? What papers have we discussed that relate to spatial scales? (hint: Garcia et al, and White et al.) How do the following differ from one another: GSDR, LSDR, and CCSR?

From Gotelli:
What are the differences between stepwise (discrete) and a continuous population growth models? What are the assumptions of each model? How do these vary from assumptions of logistic models of population growth? Are these models realistic? For what organisms would you use these models? What is r? What is lamda? What does it mean if lamda >1? What does this tell you about the value of r? What is the doubling time? What is stochasticity? How can it be quantified? Under what circumstances (values of r and variance) will a population crash? Why does demographic stochasticity have a disproportionately greater effect on small populations?

What are the differences in optimal foraging between generalists and specialists? Give examples of each. Draw an optimal foraging curve and label key points: travel time, optimum travel time and energy gain (how does this relate to the marginal value theorem?), and axes. What are assumptions of the Optimum Foraging model? What are examples of optimum foraging from the literature (at least 2)? What is prey switching? Why would it occur? Give an example from the literature (hint: bluegill sunfish & daphnia).

What are two main life history strategies? Give examples of semelparous and iteroparous organisms. Why do we see episodically iteroparous trees? How does masting relate to seed dispersal? Relate to Hollbrook & Loiselle. How might seed dispersal strategies differ between k- and r-selected species? Give an example of each.

vocab:
stochasticity
Bergman's rule
specialist
generalist
semelparous
iteroparous
clonal
colonial
modular
concordance
fragmentation
PRC
gamete
colonization
n-dimensional hypervolume

Cool links

Hey all,
Prof. Witman asked me to put up these links. The first is a news article about rapid evolution of fish to toxins in the Hudson River, and the second is a Science podcast discussing the article.

article: http://green.blogs.nytimes.com/2011/02/18/speedy-evolution-indeed/#more-92356 and http://www.poughkeepsiejournal.com/article/20110220/NEWS01/102200362/Hudson-fish-adapts-fast-to-resist-PCBs

podcast: http://www.sciencemag.org/content/331/6019/956.2.full

Question about Scaling

Hi Everyone,

I received this question about scaling relationships and I figured that I would share the answer with everyone because it might be kind of useful to others who are confused by what they wrote down in their notes:

"I saw in notes that we need to know a general form of the scaling equation..i cannot find it in my notes or the slides...could youhelp me out with this?"

And here is my response:

The most general form of a scaling equation is simply referring to the relationship between two variables. In the context of what we have talked about, these are usually in logarithmic relationships and refer to things such as body mass, average population density, etc. - however, a scaling relationship doesn't have to fall within these categories.

For a basic logarithmic scaling relationship between any two variables, we can represent it with the generalized equation:
Y = Y0X^(b) [that is "Y equals Yzero times X to the b power"]

Prof Witman probably wrote it in class with an M instead of the X, because one of the variables is usually body mass. So again, the equation that you most likely should have seen on the board would look like:

Y = Y0M^(b)

which can be written in a logarithmic form by taking the log of everything (or the natural log):

log(Y)=log(Y0) + b*log(M)

[aside from the log bit, this should remind you of algebra class and everyone's favorite equation for a line "y=mx+b": this graph looks like a straight line; whereas the other form is a log graph, which can be harder to interpret.]

Okay. So that's the general equation logarithmic scaling equation. Hopefully you should have some ideas about what it means, but just in case I've confused you, here are some places to start thinking about it.

Y0 is a constant. It's like a starting point. In the log/log scale plot, it will, in fact, be the y-intercept of the graph.

b is also a constant, and it's the interesting part of the equation. It serves to relate the two variables - so ecologists want to find b and try to think about the implications of it,and why it is so. In the log/log plot, it will be the slope.

Therefore:

if b=1, the relationship between the variables is directly proportionate; the slope is 1; if mass increases by a certain amount, the Y also increases by the same amount. This isisometric scaling.

if b does not = 1, the relationship between the variables is not directly proportionate; the slope is not 1; and if the mass increases by a certain amount, the Y will change by a different amount. This is allometric scaling.

I think I will leave off there and hopefully your notes, and the White et al review paper can help you fill in the gaps as far as examples and relevance. Again, body mass is the variable that we talked about most, but this is actually a general concept, so it can be used for other ideas as well.

Let me know if you have more questions.

Populus software

The Populus software is available for download here: http://www.cbs.umn.edu/populus/. You will need it for the second homework assignment.

Exam 1 Review Sessions

The first exam will be held in class on Thursday, Feb 24.

The TAs will be holding two review sessions at the Science Center:
Monday, Feb 21, 7-9pm
Tuesday, Feb 22, 6-8pm

Please bring specific questions on lecture or discussion material!

Peer Review Process

We are instituting a voluntary peer-review process for the grant proposals. Although optional, it does have an effect on everyone, so please read the following:

- Grant proposals are now due 2 weeks after the paper has been discussed (regardless of participation in the peer review process)

- If you are interested in participating, please sign up during section this week or email your TA.

- Participants in the process will commit to the following:

1. Turning in an electronic draft of their proposal 1 week after presenting the paper (thus, 1 week before it is due). Please email this by 5pm.

2. At some point during the semester, reading and providing helpful comments on an anonymous peer's proposal. Unfortunately, you will not know before hand when you are expected to do this. You will have 2 days after the TA emails you to review the paper and email it back to them.

3. Upon receiving their anonymously-reviewed paper, the original author will have the remainder of the second week to revise the proposal and submit it by 5pm/during section the day that it is due.

Again, participation is optional, however it is highly recommended that your participate. For a little more work, you will probably benefit from receiving and giving critiques of experimental designs and written proposals.

Update: Guidelines for writing the peer review posted on MyCourses. -Natalie

Sample Grant Proposal #2

I. Introduction
Altieri et al. (2009) points out that species evenness, in addition to the more heavily researched species richness, is an important component of biodiversity that can influence ecosystem functions and services. The researchers manipulated varying densities of consumers in natural marine ecosystems and discovered that through herbivory, consumers can control the species evenness and thus species diversity of these ecosystems. Altieri et al. found that, “both algal species evenness and biomass-specific productivity were higher in tide pools with herbivorous snails than pools where snails were absent” (p. 3). Hence, future studies on ecosystem biodiversity should be evaluated in terms of trophic interactions and species evenness in addition to the more conventional component, species richness.
The authors understand the importance of evenness on ecosystem function, and their paper addresses “ecological interactions such as herbivory that generate natural patterns of evenness and richness” (p. 1). Their experimental design differs from previous studies on the effects of evenness on ecosystem function. Such studies were primarily conducted on experimental plant communities consisted of direct manipulation of species evenness and subsequent studies of its effect on primary productivity. However, instead of supplementing previous studies by conducting a study on natural terrestrial ecosystems, Altieri et al. conducted a study in a natural marine ecosystem. Trophic interactions of terrestrial systems may differ from those of marine systems, as demonstrated by a study that found insect herbivory decreased plant evenness provided they grazed on moderately abundant species (Mulder et al., 1999). However, the study was conducted on seminatural grassland instead of a completely natural plot, which while capable of producing useful results, is not ecologically realistic.
Results of ecologically realistic experiments on terrestrial ecosystems to investigate the effect of insect herbivory on the primary productivity and biodiversity could be different from results obtained by Altieri et al. However, I predict that in terms of biodiversity, a terrestrial study on similar concepts will yield observations comparable to those seen in the natural marine ecosystems. This of course depends on the specific species consumed during consumer grazing and its relative importance in the ecosystem of interest. In terms of primary productivity, I suggest that grasshopper influences on nutrient cycling could act to increase productivity as a function of grasshopper density. This study is designed to elucidate the relationships between insect herbivore density, primary productivity and biodiversity as measured by species evenness.

II. Purpose/ Significance
The purpose of this experiment will be to investigate the effect of insect herbivory on the primary productivity and biodiversity of a terrestrial ecosystem in a manner similar to that which was used in the study conducted by Altieri et al. The question addressed will be whether insect grazing will increase species evenness and productivity. This experiment will manipulate the densities of grasshoppers, an insect herbivore common to grassland ecosystems. Through nutrient cycling, grasshoppers play an important role in the functioning of shortgrass prairie ecosystems (Mitchell & Pfadt, 1974). Nutrient cycling has been an important phenomenon associated with biodiversity (Tilman et al., 1996). Taken with the results of the study conducted by Mulder et al., grasshopper grazing should be expected to impact the biodiversity and productivity of grassland ecosystems.
This experiment will allow for greater insight into the intertrophic processes that impact grassland productivity and biodiversity. While there are numerous studies that have been conducted proving the validity of investigating species evenness as a component of biodiversity (Wilsey & Potvin, 2000, Wilsey et al., 2005, Kirwan et al., 2007), few studies examine the effect of insect herbivory on primary productivity through species evenness as a measure of biodiversity. Grasslands are not only habitats for a large number of species, but they are also an important resource in grazing economies (Branson, 2006). Worldwide, grasslands are also listed as one of the most critically endangered ecosystems (Noss, 1995). Thus understanding the ways in which consumers such as grasshoppers affect grassland productivity and diversity is important for economic as well as conservation reasons.

III. Hypothesis
While grasshoppers are voracious herbivores whose grazing results in considerable damage to grasses, which they feed on predominantly, grasshoppers often harvest more plant biomass than they consume (Beckerman et al., 1997, Belovsky et al., 2000). Nutrients from the unconsumed plant mass as well as grasshopper waste contribute to the availability and distribution of litter in the grassland ecosystem (Belovsky et al., 2000). Based on this mechanism of nutrient cycling, grasshoppers would contribute positively to grassland productivity. Herbivory due to grasshopper foraging could affect grasslands by the selective feeding of grasshoppers on specific plant species. The resulting effect would be based on characteristics such as the relative dominance and disease susceptibility of the preferred plant species (Belovsky et al., 2000). If the preferred species is a dominant grass, its targeted consumption would remove some of the competitive pressure exerted by the dominant species and could lead to greater species evenness (Hillebrand et al., 2008).

IV. Methods
The experimental design will examine the influence of grazing Lubber grasshopper (Brachystola magna) abundance on diversity and productivity of shortgrass grassland in one square meter plots of the northern Grama-Buffalograss Prairie in northwestern Kansas. The effect of grazing by Lubber grasshoppers, native to the western Great Plains prairie (Smith, 1954), on the ten following native grasses and forbs will be studied: blue grama (Bouteloua gracilis), buffalograss (Buchloë dactyloides), western wheatgrass (Agropyron smithii), rosin weed (Grindelia squarrosa), prairie phlox (Phlox andicola), wild alfafa (Psoralea tenuiflora), prairie coneflower (Ratibida columnifera), scarlet globemallow (Sphaeralcea coccinea), sand dropseed (Sporobolus cryptandrus), and needle grass (Stipa comata), of which blue grama and buffalograss are the two dominant species (Küchler, 1974).
Lubber grasshopper field densities will be determined by counting grasshoppers in quadrants and fifty 1 m2 experimental plots will be randomly assigned to each of the three grasshopper densities: 0 individuals per m2, 1x field density, and 2x field density. Grasshopper densities will be established maintained by direct manual removal and addition of similarly sized insects. To minimize grasshopper immigration and emigration, roofless cages of fine stainless steel mesh 2 m high will be constructed and secured around the plots using washers and bolts. The experiment will be carried out during the period of one year to collect data in the most ecologically realistic manner possible.
Staggered biweekly measurements of species evenness will be taken to determine the effect of grasshopper density on biodiversity. Primary productivity will be quantified with aboveground net plant production (NPP), to be measured with a radiometer.

V. Interpretation of Results
A. Null Hypothesis

The null hypothesis for this experiment in terms of diversity is that there is no variation in species evenness between plots with 0 individuals per m2 and 2x field density compared to the control of 1x field density. This might occur if the grasshoppers fed in a general manner without displaying preferential feeding for any specific species or if the grasshoppers fed more heavily on the non-dominant species of grasses and the shift in numbers did not result in a change in species evenness. In terms of productivity, the null hypothesis for this experiment is that there is no difference between NPP of each of the different plots.

B. Alternative Hypothesis

If a correlation were found between grasshopper density and diversity, there would be differences in species evenness between the three different plots. If the grasshoppers fed more heavily on blue grama or buffalograss, a positive correlation between consumer density and biodiversity would be expected. If the grasshoppers fed more heavily on the other species of grass, a negative correlation between consumer density and biodiversity would be expected. This would occur presumably by reducing the numbers of less competitively dominant species, freeing resources and space which give the dominant buffalograss and blue grama and opportunity to increase in number, reducing species evenness and biodiversity. If a correlation were found between grasshopper density and productivity, there would be differences in primary productivity between the three different plots.
VI. Bibliography

Altieri A. H. et al. (2009). Consumers control diversity and functioning of a natural marine ecosystem. PLoS ONE 4(4): 1–5.
Beckerman, A. P. et al. (1997). Experimental evidence for a behavior-mediated trophic cascade in a terrestrial food chain. Proceedings of the National Academy of Sciences (USA) 94: 10735–10738
Belovsky G. E. et al. (2000). Grasshoppers—plus and minus: The grasshopper problem on a regional basis and a look at beneficial effects of grasshoppers. Pages VII.16.1–VII.16.5 in Cunningham G. L., Sampson M. W., eds. Grasshopper Integrated Pest Management User Handbook. Washington (DC): US Department of Agriculture, Animal and Plant Health Inspection Service. USDA/APHIS Technical Bulletin 1809.
Branson, D. H. et al. (2006). Sustainable Management of Insect Herbivores in Grassland Ecosystems: New Perspectives in Grasshopper Control. Bioscience 56(9): 743–755.
Hillebrand, H. et al. (2008). Consequences of dominance: a review of evenness effects on local and regional ecosystem processes. Ecology 89(6): 1510–1520
Kirwin, L. et al. (2007). Evenness drives consistent diversity effects in intensive grassland systems across 28 European sites. Journal of Ecology 95: 530–539.
Küchler, A. W. (1974). A new vegetation map of Kansas. Ecology 55(3): 586–604.
Mitchell, J. E. & Pfadt, R. E. (1974). The role of grasshoppers in a shortgrass prairie ecosystem. Environmental Entomology 3: 358–360.
Mulder, C. P. H., et al. (1999). Insects affect relationships between plant species richness and ecosystem processes. Ecology letters 2: 237–246.
Noss, R. F. et al. (1995). Endangered ecosystems of the United States: a preliminary assessment of loss and degradation. U.S. Dept. of the Interior, National Biological Service
Smith, R. C. (1954). An analysis of 100 years of grasshopper populations in Kansas (1854 to 1954). Transactions of the Kansas Academy of Science 57(4): 397–433.
Tilman, D. et al. (1996). Productivity and sustainability influenced by biodiversity in grassland ecosystem. Nature 379(22): 718–720.
Wilsey, B. J. & Potvin, C. (2000). Biodiversity and ecosystem functioning: importance of species evenness in an old field. Ecology 81(4): 887–892.
Wilsey, B. J. et al. (2005). Relationships among indices suggest that richness is an incomplete surrogate for grassland biodiversity. Ecology 86(5): 1178–1184.

Sample Grant Proposal #1

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Grant Proposal 1

Introduction

Global anthropogenic climate change is expected to result in dramatic transformations of the Earth’s atmosphere and climate. These include increased levels of atmospheric CO2, increases in the number and severity of extreme weather events, increased precipitation, and elevated temperatures resulting in fewer cold days and more hot days (IPCC 2007). The altered climate could have significant effects on plant phenology because the timing of plant growth and reproduction is influenced by temperature and light (Partanen et al. 1998). In addition, earlier frost retreats and earlier leafing (Schwartz et al. 2006) result in an accelerated spring and a longer growing season for many plants.

While the effects of climate change on plant phenology are relatively well-documented, few studies have sought to demonstrate how plant trophic interactions may be affected by climate change. Of particular interest is the question of whether the phenologies of plants and their trophic links will be synchronous. This will be influenced by the specific environmental cues that have heavy effects on each trophic level. For example, plant phenology may be most affected by CO2 levels while the phenology of one of its trophic interactors may be more directly influenced by temperature. Differences such as these in the principal environmental cues affecting phenology may result in asynchrony between trophic links. Asynchrony could have significant cascading consequences on trophic interactions within a community. Two trophic interactions of interest are the mutualistic relationship between plants and their pollinators and the predatory relationship between herbivores and plants.

Several studies have addressed how climate change may affect plant-pollinator interactions via phenological changes. Hegland et al. discuss several possibilities of the results of global warming on these interactions. One possibility is that plants and their pollinators will experience parallel phenological responses, since flowering onset in plants and first appearance dates of pollinators seem to be advancing in a linear fashion in response to increases in temperature. However, warming may also result in ‘temporal mismatches’ between plant phenologies and the phonologies of their pollinators (Hegland et al. 2009). This reduced overlap in time may limit plant pollination and therefore reduce the plant’s fecundity and the pollinator’s food source.

By influencing their phenologies in different ways, climate change may disrupt or even eliminate the mutualistic interactions between plants and their pollinators. One study which simulated a doubling of atmospheric CO2 found that the amount of floral resources available was reduced for at least 17% and up to half of all pollinator species (Memmott et al. 2007). This could result in the end of mutualism or even the extinction of certain plants and/or their pollinators.

There have also been studies which document how herbivore-plant interactions may be affected by phenological shifts caused by climate change. Post and Forchhammer used data on timing of calving by caribou (a migratory herbivorous species) and timing of plant growth in Greenland, in an area where temperatures had risen by more than 4°C since 1993. They found that the caribou could not adjust to earlier plant growth seasons and as a result, offspring production dropped fourfold. This was a consequence of the trophic mismatch between caribou and plant phenologies due to the fact that the caribou’s migration into summer ranges is cued by changes in photoperiod (day length) while the start of the plant-growing season is cued by changes in temperature (Post and Forchhammer 2008). In general, offspring production by herbivores is timed to coincide with the annual peak of plant growth to optimize food resources for the new generation. As plant phenology is accelerated by global warming, a trophic mismatch may occur as the peak of resource demand by herbivores does not match the resource availability of plants. This may result in population declines in herbivores.

While the effects of climate change on phenology-mediated trophic interactions of plants have been studied for the plant-pollinator and herbivore-plant relationships, little is known about the net effect these interactions will have. Studies incorporating both pollinators and herbivores could paint a more realistic picture of how climate change may actually cause trophic mismatches and affect trophic interactions.

Purpose/significance

The purpose of my experiment would be to incorporate plants and both herbivores and pollinators to get an idea of how climate change may actually affect trophic mismatches due to phenology shifts. We have some idea of how plant-pollinator interactions could be affected by climate change and some idea of how herbivore-plant interactions could be affected, but in nature, both pollinators and herbivores exist in one system. Therefore, in order to get a good idea of how climate change will affect trophic interactions in situ, experiments must incorporate herbivores and pollinators in one system with a plant.

This experiment could be significant in several ways. First of all, the experiment could yield a realistic picture of how global climate change will actually affect plants as trophic interactors. This would be useful in obtaining a sense of whether or not climate change will cause dramatic changes in communities and ecosystems. Second of all, as primary producers, plants play an extremely important role in providing fuel for the entire community. Understanding how other species interact with plants is vital to understanding how plant abundance and success will be affected by climate change. Thirdly, as some studies have suggested, climate change may result in trophic mismatches due to changes in phenology which could adversely affect mutualistic relationships such as the one between plants and pollinators. How plant-pollinator interactions are shaped by climate change may then serve as a model for how other mutualist pairs in nature may be affected. These climate-driven trophic mismatches could also open the door for exotic species to invade – if an exotic pollinator or herbivore has a phenology better synchronized to a local plant, it is likely to outcompete local pollinators/herbivores which are out of sync with the plant phenology (Ward and Masters 2007).

Finally, our food supply is largely composed of plants (fruits, vegetables, legumes, grains, etc.) and herbivores (poultry, cattle, etc.). Knowing how climate change will affect plants and consequently the food supply of herbivores is therefore crucial to understanding how our food supply may be affected by present and future global climate change.

Hypothesis

The overarching question my experiments will address is “How does climate change affect trophic interactions between plants, pollinators, and herbivores?” This would incorporate three main testable questions:

What climatic/atmospheric factors drive the phenologies of each of the trophic links?”

Do trophic mismatches occur when the three (plant, pollinator, and herbivore) occur in one system?

If trophic mismatches do occur, what effect do they have on the success of each of the species?

I would hypothesize that (1) different climatic factors will affect plants, pollinators, and herbivores differently (for example, concentration of CO2 in the atmosphere may have a large effect on the timing of plant growth but no noticeable effect on herbivores); (2) trophic mismatches will occur when the three occur in one system because the phenology of each is driven by different climatic factors; and (3) trophic mismatches will negatively affect the initial and future population of herbivores and pollinators and the next generation of plants (because of lack of pollination). The null hypothesis would be that the same climatic/atmospheric factors affect plants, pollinators, and herbivores equally and therefore no trophic mismatches occur.

In general, a decline in success of all three species is expected if there is trophic mismatch between the three. This could be mitigated by rapid adaptation, but this is unlikely. Because of the complexity of interactions between plants, pollinators, and herbivores, there may be several alternative hypotheses for the mechanisms driving the decline in success.

Methods

For my experiment I would use species naturally occurring in the northeastern United States. I would use a common wildflower, the Black-Eyed Susan Rudbeckia hirta (annual, early-blooming species; requires full sun and average moisture). For the pollinator I would use the American bumblebee Bombus pennsylvanicus and for the herbivore I would use the white-tailed deer Odocoileus virginianus.

I would first test which climatic factor affects the phenology of each species in isolation. I would grow 2 replicates of four plots (each plot 2 by 2 meters) of the Black-Eyed Susans: one plot would have doubled CO2 concentrations (over ambient), one would have temperature elevated by 1°C above ambient, one would be covered on top for several hours so as to simulate a shorter photoperiod, and one would be a control with no treatment. All would have the same soil and access to pollinators. Time of first budding would be recorded for each treatment and serve as an indicator of how phenology is affected by each treatment.

To test which climatic factors directly affect the phenology of bumblebees, I would have 2 replicates of four groups of bees. Each of the eight pens would include a hive of approximately equal size and a small plot of land (5 by 5 meters). The treatments would be the same as above: one group would be exposed to doubled atmospheric CO2 concentration, one would be exposed to higher temperature, one would have a simulated shorter photoperiod, and one would be a control group. To make sure any effects are indirect and not due to the plants whose pollen the bees feed on, the plants given to the bees to feed on would not be exposed to the experimental treatments (they would only be inserted into the bee pens at certain times – twice a day, etc.). The peak bumblebee population times would be recorded for each treatment and be indicators of how phenology is affected by each treatment. The setup for the white-tailed deer would be similar to the bumblebee setup with 1 replicate of four pens (pens much larger – 100 by 100 meters) and the same treatments as above. For each of the experiments, the density of the species should be similar to ambient. The experiments should be run for three years.

To determine how the three species interact, all three should be placed at ambient densities in four pens (100 by 100 meters) and each pen should receive the same treatments as above. First budding times should be recorded for plants and peak population times should be recorded for bumblebees and white-tailed deer.

Interpretation of Results

If the null hypothesis is true then either none of the treatments should have any effect on the phenologies or the treatments should have an equal effect. If this is not the case, then different climatic factors affect phenology in distinct ways. If the three species phenologies are affected by different environmental cues, then trophic mismatch can be expected. When the three species are put into one system, trophic mismatch should result in general decline of the community since pollinators and herbivores will not be abundant when plants flower and the next generation of plants will also be hurt due to the lack of pollination. If there were no decline in any of the species when put into one system even though the idea of trophic mismatch predicts it, this would serve as evidence against my hypothesis.

Literature Cited

Hegland, S. J. et al. (2009) How does climate warming affect plant-pollinator interactions?

Ecology Letters. 12(2): 184-95.

Intergovernmental Panel on Climate Change. Contribution of Working Groups I, II, and III to the

Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Pachauri,

R. K. and Reisinger, A. (Eds.) Geneva, Switzerland. 2007.

Memmott, J. et al. (2007) Global warming and the disruption of plant-pollinator interactions.

Ecology Letters. 10(8): 710-7.

Partanen, J. et al. (1998) Effects of photoperiod and temperature on the timing of bud burst in

Norway spruce (Picea abies). Tree Physiology. 18, 811–816.

Post, E. and Forchhammer, M.C. (2008) Climate change reduces reproductive success of an

Arctic herbivore through trophic mismatch. Philosophical Transactions of the Royal

Society: Biological Sciences. 363(1501): 2369-75.

Schwartz, M.D. et al. (2006) Onset of spring starting earlier across the Northern Hemisphere.

Global Change Biology. 12, 343–351.

Ward, N.L. and Masters, G.J. (2007) Linking climate change and species invasion: an illustration

using insect herbivores. Global Change Biology. 13(8): 1605-15.