J.L. Harper A Darwinian Approach to Plant Ecology

So if Ehrlich and Raven were focused on the competitive nature of plant versus herbivore with insect larva as an example, John L. Harper is considering plant to plant interactions alone. He does admit to completely avoiding the topic, but the paper is quite full without the consideration of herbivores on plant communities.

This paper is synthesizing the application of population and community structure to the study of plant ecology. He is bringing in plenty of previous equations and graphs to look at plant community structure. Until now, plant communities have been described qualitatively without empirical data. Harper is concerned with measuring the science of plant ecology and collects data from many different studies to put this paper together.

As he mentions at the beginning, many plant ecologists had not taken the same approach to population ecology as the animal ecologists. Plasticity and vegetative reproduction complicate the study of discrete plant populations when compared to animal populations. Even at a similar age, two plants can have different sizes and weights depending on the quality of the environment it grew within. However, Harper still describes plant population growth with the logistic model: dN/dt=rN((K+N)/K). 

Harper mentions that r, intrinsic rate of increase, now has two forms of increase for higher plants, seed output and vegetative reproduction. Seed output has a broader geographic range and vegetative reproduction is an immediate and proximate increase in population size. K represents the literal density of the plant coverage in relation to how much sun the plant can obtain. There is only so much surface area that is usable for photosynthesizing and producing dry weight. In fact, using the dry weight of plant parts, Harper shows the relative energy distribution between plant organs within a couple of species over the growing season. The mass reflects the amount of energy devoted to growing that particular organ.

Additionally, the more the plant grows the more it crowds out others, which can be derived from the equation, 1/w=a+bx. Mean plant weight is represented with w and density is x.  This is the reciprocal yield law; the mean plant weight, w, and density, x, share an inverse relationship. The smaller the mean plant weight allows for a higher the density and vice versa. There is only so much room for plants to grow and only so many nutrients and moisture in the soil. Plants are controlled by density-dependent limitation with each other. This point is further made with effects on density due to frequency distribution in Figure 8. The higher the density of seeds sown the higher the frequency of lower weight plants.

Darwin emphasized intraspecific competition, or interspecific competition between closely related species, as a means of evolution. The competitive edge that one particular individual has over other individuals of its own species for limited resources provides it improved fitness. The mechanical abilities for a plant to gain leaf coverage for the sun, tap into soil for nutrients and water allows it to outgrow other individuals. Secondary metabolite production gives the plants a chemical advantage via herbivory reduction and direct attacks on other plants. The winners of the competition provide the opportunity to reproduce.

Harper presents a modification to Hutchinson’s logistic growth for two species that live together. When the density of different species differ then there is a shift from inter- and intraspecific competition. The lower density species faces interspecific competition with the higher density species more than intraspecific competition within its own species, however the higher density species faces intraspecific competition with little interspecific competition with the less abundant species. This feels intuitive. However, it is not to say that intraspecific competition does not exist for the less abundant species, but that the weight of intraspecific competition probably does not compare with interspecific competition.

Yet despite the density-dependent factors and competition of plant community structure, higher diversity within a plant system is more productive than a single species system. The one animal example Harper uses with Drosophila helps illustrates this point; the dumpy and wild type flies do not interfere with each other’s population size. Ecological combining ability increases niche specialization and diversity. The increase of diversity may also mean the increase of stability for the mixture of populations. As Harper mentions, better exploitation of the resources of the environment helps strengthen the community. 

Helpful & fun resources for grad core ecology

I've come across a few things recently that I thought some of you might enjoy or find useful:

• ESA Centennial Papers: For its 100th birthday the Ecological Society of America has put together a collection of the most cited/downloaded papers from its journals. Some will be familiar. A few have interesting commentary blurbs. Not an exhaustive survey, but probably relevant when considering where the field has gone since Foundations of Ecology was published.

• Animated Foundations of Ecology: Some fun little animations of ideas from a handful of the foundation papers, including one for HSS!

• Science Careers MyIDP: For anyone interested in more professional development resources, this site is very helpful for self-evaluation, goal-setting, and career exploration. It is geared more towards post-docs, but I think is still useful for grad students wanting to enter the research world.

Theory of Feeding Strategies - Thomas W. Schoener

Schoener's paper may be the first proper review paper that we've come across. It's dense, long and spattered with math, and one gets the feeling while reading that Schoener has spent a long time pouring over his colleagues' work. The paper itself spans a number of topics related to feeding strategy, and in the interest of brevity I will try to be short in summation.

 General Model of Feeding Strategy
 Schoener starts with the basics of time vs energy, describing two types of feeders. Type I is an organism that searches for food while partaking in other activities such as searching for mates, predator avoidance or displacing invaders, and therefore does not expend energy exclusively on searching. Type II is an organism for which searching for food and other activities are mutually exclusive. Both types must optimize their diet based on the following equation for individual prey items: (e/t)=(Potential Energy - pursuit cost - handling costs)/(Pursuit time + handling time)
If any one prey item's net energy is greater than the feeder's energy requirement (M), then the feeder should focus on that item. If a single prey item does not fulfill the feeder's energy requirement, the feeder should add different items in order of highest to lowest (e/t) to reach its energy requirements. For Type II feeders, any time spent traveling to feeding grounds before any energy is gained must also be taken into account. For them, a balance must be found between searching for food and performing other activities. Either type should optimize their diet in order to increase fitness. We see the gain in fitness by looking at the relation of increased energy to basal metabolic rate. Any diet must satisfy basal metabolic rate, which is related to body weight (W), before the organism can focus on growing or breeding. Large animals tend to be more efficient in storing fat reserves, and so can generally focus less on this basic upkeep. Once BMR is satisfied, optimal diet will benefit organisms by increasing growth rate, and potentially decreasing the time before reproduction, in which case their optimized diet will also benefit their offspring, increase broods per season and individuals per brood. Generally speaking, an organism should try to maximize energy obtained from their diet, however, the benefit from maximizing energy must be balanced with the benefit from performing other tasks such as mating. After all, it doesn't matter how much energy you could potentially give to your offspring if you never actually have any. As such, Schoener points out that feeding time and success in mating are inversely proportional. This is not to say that optimal diet is an unwavering standard for species. Each organism, regardless of feeding type, moves through a progression of feeding periods in which the organism should strive to optimize their net energy based on the requirements put upon them in that feeding period. The optimal diet should be recalculated every period. Optimization can be further viewed based on whether the organism is a time minimizer (generally organisms with a fixed reproductive output) or an energy maximizer.

 Optimal Diet
Schoener here moves on to an examination of the optimal diet. In order to be feeding optimally, organisms must choose an optimal diet, which is based on search time, pursuit time, search and pursuit energy expenditure, pursuit and capture success, potential caloric content and relative abundance of food items. Schoener presents a number of different models for optimal diet proposed by other authors. Hollings examines which prey would be mechanically easiest to predate, MacArthur and Pianka look at the forces of natural selection on eating as a function of pursuit time and search time. Emlen considered when an organism should pass on a prey item if they were aware of another prey item available and Levins & MacArthur examine how much selectivity in diet is most beneficial to the production of offspring. The general consensus being that food scarcity should increase the variety of prey items eaten. Feeders should take larger items closer to their origin before moving further to eat smaller items, and smaller predators should take smaller prey. Furthermore, the more you travel to find food, the more specialized you should be to increase your energy intake; larger organisms should be more specialist for the same reason. Generalism is favored, however, where food items are affected deferentially and unpredictably. Interestingly, Schoener asserts that when all food items are affected equally by feeders, feeders should converge in size.
 Schoener concludes the section with a short discussion of feeding rates, siting Holling's "functional response", which maps three separate intake rates based on food density. The first, linear model based on Lotka-Volterra, being most similar to a filter feeder, is an organism for which eating does not interfere with searching for food. The second is an organism which is limited by its ability to process food. We might think of something like a lion, which could theoretically catch many items when prey density is high, but then must stop capturing prey in order to eat. Finally, the third, making a similar plot to the second, is an organism that is limited by food processing, but also either learns or switches food based on density. This would be something like a honey bee, which must first search for food, but then once food is found remembers its location.

 Optimal Forage Space
Here we explore spacial range for an organism, which is again based on food density, as well as selectivity and metabolism of the feeder. Schoener explains that for any area in which food items are not uniformly distributed, the range size must exceed the size expected based on the above criteria to adjust for food patchiness. Here an organism's connectedness comes into play; the range size for a more connected organism being smaller than that of a less connected organism. Larger organisms should have larger ranges. Carnivores generally have larger ranges than hunting herbivores, which have larger ranges than browsers and grazers, which is based on level of specialization and food density. Efficient pursuers should have larger ranges because it is less energetically expensive for them to pursue prey. Rather than increasing range size, a decrease in food density causes a decrease in the range of items taken from the same area- organisms become more specialist. However, according to "compression hypothesis", the range of patches searched by feeders will decrease when food items are reduced deferentially based on the quality of the patch. Finally Schoener discusses the relation of food resources on territoriality, and the energy an organism should expend to protect a food source. Basically, when invasion is very low, and food resources are dense, there is no reason to protect food. Likewise, when invasion is common and food is scarce, the benefit of protecting a food source is outweighed by the energy expended chasing off invaders. Therefore, range should only be protected when invader frequency is below a certain level, and when food is neither in severe abundance or rarity.

 Optimal Feeding Period
Schoener very briefly discusses when organisms should expand their feeding period, or should utilize more energetically expensive feeding strategies. Generally, when food is scarce, when the feeder has a large energy requirement, or when the feeder has the best opportunity to reproduce, they should expand the feeding period.

 Optimal Foraging Group Size
Finally Schoener explores the role of gregarious behavior in feeding strategy. He presents three potential reasons why organisms may forage in groups and support behind the theories. Each depends on foraging efficiency, risk of predation and the defensible area and its unit cost of defense. The first type of group foraging decreases foraging efficiency, but it is energetically beneficial because it improves another aspect of the organism's life, such as navigation, breeding potential and a decreased risk of predation. The second type has no affect on foraging efficiency, but occurs where food resources are clumped such that multiple individuals can obtain more food together than any could foraging elsewhere alone. And the third type of group foraging increases foraging efficiency by flushing or driving prey, or simply by increasing the size of the food item that can be taken by the feeders collectively. All types of group foraging can be further beneficial by decreasing the overlap in home ranges, thereby decreasing the energy and time spent protecting the area, and decreasing time spent searching a large area. Groups can collectively increase the maximum range of any individual.

Questions:
Schoener makes the assumption in the general model that any organism that does not feed during a period has a reproductive output of 0. Obviously if an organism starves to death, or has no energy to mate, this is true. Are there cases where it is not?

 Is the expenditure of energy in hunting really proportional to BMR? Are there any organisms that function outside the proposed (lambda) of 0.5 to 1?

 Schoener asserts that larger organisms should be more specialized and have larger ranges. Does our knowledge of the past confirm this?

Why would feeders converge in size when food items are uniformly reduced?

P.R. Ehrlich & P.H. Raven Butterflies and Plants: A Study in Coevolution

Paul R. Ehrlich and Peter H. Raven present a detailed account of butterfly genera and the specific plant genera they feed on. The point of the study was connect the biochemical (and mechanical) adaptations of herbivores, with insect larva as a particular example, to the plant as a means of diversification for the herbivores. Ehrlich and Raven also mention how radiation of plants has been greatly affected by the development of these biochemical defenses via secondary metabolites.

Frankly, I barely skimmed over the eight pages of genera matching, because other than the table of butterfly taxonomy I had no frame of reference for plenty of the plant taxonomy. A visualization of the matching would have been rather helpful. Most of what I got from those pages was how certain compounds in the plants designated which butterfly larva would eat them. Additionally, I did take note of the presence of angiosperms due to the mentioning of monocotyledon and dicotyledon.

Angiosperm radiation would have provided a platform for insect herbivore radiation based on the notion of plant diversity providing more niches for the herbivores to occupy. There are generalists to be sure, but there are many specialists who form tight connections with the particular plant they feed off of. As the diversity of the plants increases, the different types of compounds increases. Which then leads to the increase of insect diversity due to resistance to the specific secondary metabolites of a particular plant.

When I first started reading this paper, I was expecting the main focus to be pollination. It was interesting to find that Ehrlich and Raven focused on the metabolic connection of butterfly and moth larva on plants. Perhaps I am coming from a perspective that does not weigh insect herbivory all that well? I just think that another consideration to this coevolution study is missing.

The ability of the caterpillars to be able to coevolve and/or adapt to certain genera of plants due to mechanical and chemical compatibility is important, but the larva is only one stage to the insect’s life. I’m sure the affinity of a butterfly larva to a particular plant would then mean the adult insect would more than likely pollinate that particular plant. However, pollination is only mentioned once, briefly, and in reference to wind-pollination.  I think about how there are some many animals that have become finely tuned in their ability to take advantage of a particular plant’s flowers for food and how those animals then act as pollinators to increase genetic diversity in the angiosperms. 

The reviewing process from the NSF perspective.

This is taken from the NSF DEB Blog (slightly abbreviated):

For a while now we’ve been pondering how to approach a delicate subject: writing reviews. This subject is something of a minefield of tensions, conflicts, opinions, and opportunities to offend, alienate, and otherwise ruffle feathers by implying, “you’re doing it wrong.” So we feel it is important to explain here why we are tackling this topic and mention some of the approaches that didn’t fly. 

We receive requests from less experienced reviewers who want advice outside the trial-by-fire of an actual panel to hone their review-writing skills. And we also hear from PIs who are disappointed in the utility or quality of the reviews they have received.To set the tone, let’s make a couple of blanket statements straight off. You are all volunteers in this effort and you do a great job and deserve immense gratitude. With that said, probably every established PI who has submitted NSF proposals has received at least one review that was… a bit less than they expected; perhaps lacking a certain degree of usefulness; or even a complete enigma.

What is going on with these sub-par reviews? Well, it’s difficult to address because we don’t think there is a singular problem. Simply put, in managing the whirlwind of research life, we may occasionally fall short in creating the ideal, critical, insightful and helpful proposal review.

Why are we addressing this here? There is quite a bit out there about writing manuscript reviews, and even some nice posts about PIs coming to grips with the reviews received. But there’s a pretty big void when it comes to discussion of actually writing proposal reviews. As a program office, we have a unique platform to start a discussion around review expectations and feel it is a topic worth talking about openly. What we hope to achieve is not a static prescription for a “good review” but for this to start a discussion, raising community awareness about the importance of proposal reviews and the need for continual improvement in writing them, regardless of how well we think we’re doing already.

There are many paths to a good proposal review. But the major commonalities can be distilled into a few points.

Good proposal reviews:

• are conceived with an open mind and reflect serious consideration of the proposal

• make a compelling case for the evaluation, citing evidence or examples as needed

• are well-written and direct

• contain no ad hominem criticisms or unsupported assertions

Of course the real challenge is, “How do I do it?” To help out we’ve started a list (in step-wise order[i]) of tips to guide you past the most common pitfalls on the path to completing a great review.

• Refresh your expectations, EVERY TIME: You are asked to judge a proposal against a specific set of review criteria. These review criteria are not static. They change over time; some change almost yearly and may have changed since you last looked. They vary by organization; each successive layer of directorates, divisions, and programs has an opportunity to modify and specify review criteria; what you are asked to do in education or geosciences will differ in the specifics from what you are asked to do in DEB even though all still abide the NSF merit review criteria[ii]. They can be stacked and combined; even within a single panel some proposals will include additional review criteria relevant to a submission option or solicitation on top of those of the conceptual program at the heart of the proposal.

• Recognize your audiences (yes, plural): You, as a reviewer are always writing for two, and potentially three, audiences. Firstly, the review is to inform us (NSF) as to why you think the proposal is or is not worth funding; we consider this in light of others’ reviews and our award portfolio to arrive at a funding recommendation. Secondly, the review is to help the PI learn how to prepare a better proposal next time, or to improve the project if it is funded. Lastly, the written review may be used as documentary evidence that you as the reviewer gave the proposal thorough and thoughtful consideration.

• Don’t summarize, review: Every audience for the review will also have access to the actual proposal and doesn’t need you to summarize it; we need your well-argued opinion of it. Note: Starting out with largely descriptive text may be important to organizing your thoughts as a reviewer. That’s ok. That’s great. But it’s not the end point. What we’re saying here is: take whatever part of the proposal you felt important enough to describe and fill it out by giving your opinion of it and telling us why you hold that opinion.

• Provide clear and substantiated comments: To satisfy the distinct audiences and avoid summarizing, every point you make in your review should exhibit these 3 characteristics:

• It is EVALUATIVE

• It is SUPPORTED with appropriate details, evidence, and comparisons

• It provides CONSTRUCTIVE feedback in the context of the proposal

Notes: EVALUATIVE means using terms that express an opinion about the subject like “good”, “bad”, “excellent”, “inadequate”, “exemplary”, “satisfactory”, etc. SUPPORTED means following the evaluative term with an explanation like “because the [specific information in the proposal] is/is not reflective of [some important external knowledge like cited literature, best practice, or other relevant information].”

• Be self-critical of your critical comments: A good critical comment delves below the surface of your initial reaction and constructively reflects the opportunities and constraints for addressing the issue. This step could easily be its own separate post; we’ll provide a longer discussion of this at a later date. For now though, the fast summary is: see the flaws, point them out but give them context, and don’t get hung up on small stuff. Stop and ask: why is it a flaw for this proposal? Do I fault others for this consistently? If not, why have I pointed it out now? Is there a deeper cause of this flaw that I can describe? (If so, do it.) Search out your biases. Recognize and differentiate between actual problems in the proposal and legitimate differences from how you would do the work. Make sure your suggestions for improvement are clear and reflect the constraints on space: it’s limited, and PIs can’t include “more” material on X without cutting Y. A useful suggestion needs to identify both the X that needs expansion and the Y that could stand to be cut.

• Minimize or omit purely descriptive text: This is a repeat of point 3. We’re serious about this. It is very easy to fall into the summarizing trap – it’s much easier to write that material – and we have to remind ourselves about this constantly too. If some aspect of the proposal is important enough for you to describe, tell us how it affects your overall opinion of the proposal and why.

Community Structure, Population Control, and Competition Nelson G. Hairston, Frederick E. Smith, and Lawrence B. Slobodkin

This paper concerns itself with population control. The authors begin at the bottom, so to speak, looking at the accumulation of fossil fuels vs the rate of energy fixation by photosynthesis. They conclude that the rate of photosynthesis far exceeds the build-up of fossil fuels and that the vast majority of energy fixed by autotrophs must therefore pass through the biosphere. Next we look at the decomposers, which the authors conclude, must be food limited, as by definition they exist to degrade organic debris. Even if some decomposers are instead limited by predation, population density etc, the remaining decomposers must consume whatever is left by those non-food-limited decomposers, and as such, as a group, the decomposers are food-limited. Furthermore, any population that is not food-limited must be limited by the limitations of the level below them.
Looking next to the primary producers. Since large herbivores are not decimating populations of plants, nor are plants controlled by natural catastrophe, they must be limited by abiotic factors in the environment. Light would be the most obvious, but in arid environments, the authors note, water would likely be a limiting factor. In connection, herbivore populations clearly cannot be food limited, as they will continue to grow out of control when protected by man, decimating the plant population of the area. Furthermore, weather does not seem to be a limiting factor, unless one supposes that populations of herbivores are not capable of removing themselves from an area that has been badly effected by weather. These assumptions leave the control of herbivore populations to predation, up to and including parasitism.  

Interestingly, as predators would likely be immune to the limitations by weather and other abiotic factors that herbivores and producers are immune to, we must again assume that predators are food-limited as a group. The authors note that some predators are arguably territory-limited (although that in itself is limited by food availability).

The authors thereby reach the conclusion that terrestrial communities are resource-limited. We see the reasoning behind this by examining plant/herbivore interactions. Plants, being in competition for space, must exist in such an area where they are not regularly depleted by herbivores. Herbivores in turn, must be limited by predation in order to not deplete their food source. Interspecific competition is also a consideration, as any link in the web occupied by multiple organisms reduces the effects of predation on either.

Homage to Santa Rosalia or Why are There So Many Kinds of Animals? G.E. Hutchinson

This is a pleasant little tome by the master of the niche, G.E. Hutchinson. Having recently been named the president of Yale’s department of Zoology, his address at the annual meeting of The American Society of Naturalists begins with a recalling of a trip to Sicily, and observing two species of beetle in a small pond below the sanctuary of Santa Rosalita, whom Hutchinson proposes could be considered “the patroness of evolutionary studies” for the duration of the tale. The beetles are of the family Corixidae, C. punctata (the larger) and C. affinis (the smaller), and Hutchinson takes note that all of the observed C. punctata are female, and hence must be at the end of their breeding season, while there is an equal mix of sexes of the smaller beetle, which must be just beginning theirs. A series of questions – why are the breeding periods offset, why are there two beetles and not 200, finally leads us to the point of the story, an exploration on why there are so many kinds of animals. Rather than attempt to explain the existence of the magnitude of animals on the planet mathematically, Hutchinson decides to focus on some of the factors which control the number of kinds of animals.

Food Chains
Noting Elton’s work on predator chains and using as example what he terms the “Eltonian” food chain, in which each predator is successively bigger than its prey, Hutchinson theorizes on the number of links possible in a food chain. Generously assuming that 20% of the energy of one organism might be passed on to the next link in the chain, and that predators should reasonably be about twice the mass of their prey, Hutchinson proposes that 5 links is the maximum allowable by the Eltonian predator chain.  

Natural Selection
Natural selection, Hutchinson states, is the abbreviator of food chains, noting that any increase of efficiency of a predator at the nth link in the chain may well cause the extinction of the (n-1)th link; and that this extinction would thereby force the nth link predator to adapt to eating the (n-2)th link or itself go extinct. He notes that it would be unlikely for a new terminal predator to form an (n+1)th link.

Effect of Size
Hutchinson briefly states his view on sympatric niche development in the beginning of his address, and from that understanding he expresses the limitation on the number of links in a food chain that might be filled by organisms that change drastically in size over the course of their lives. Should an organism normally occupying the nth link in the chain at adulthood inhabit the niche of a smaller animal in its youth, the number of different kinds of animals in that chain would be severely reduced due to competition.

Effects of terrestrial plants
Here Hutchinson simply points out the incredible variety of terrestrial plants, and the subsequently incredible variation of the insects that feed thereupon. However, while this increases the overall variety of organisms, this does not much increase the number of links in the Eltonian chain.

Interrelations of Food Chains
Here we step away from the direct contemplation of food chains and into food webs. Since realistically, any predator at the nth level would have more than one prey item at the (n-1)th level, they will not eat themselves into extinction, nor fully exterminate a single prey, as once the first prey item became scarce, it would be more feasible to hunt the other. As food webs do not represent a 1:1 interaction between predator and prey, there leaves room to examine how new organisms might join the web, and its effect upon the web. Hutchinson sites MacArthur (1955), which states that the stability of a community is directly related to the number of links in the food web, that efficient organisms will displace inefficient ones, and that stable communities will outlast the unstable. He further sites that there are three ways to add an organism to a community; by displacing an existing organism, by filling an empty niche, or it may partition a pre-existing niche. The first might increase the stability of the community if it itself is a more stable organism. The second and third could provide new links in the chain, thereby increasing stability. Siting Elton again, Hutchinson adds that the most stable communities are the oldest; those which have had ample time to replace inefficient organisms and add new links, and that as time goes on, it would be progressively more difficult to add new organisms because of this stability. This, he notes, explains a bit of the overall question of diversity- organisms are diverse because assemblages of diverse organisms increase the links in food webs and make communities more stable.

Limitation of Diversity
As we now have some answer for why there are so many kinds of animals, Hutchinson goes on to ask why there are not more kinds of animals. Using arctic species as example, Hutchinson notes that overall biomass is likely a limiting factor on diversity; if the basal members of the web can only support half of the predators that a more productive area might, there will obviously be fewer links in the web. Age of the community might also play a role, as communities become more stable over time, Hutchinson suggests the arctic communities may have simply not had enough time to evolve the diversity seen elsewhere. He also notes that competition for space might limit diversity in an area, as is reasonably the case for voles in the British Isles.

Niche Requirements
Here we examine the result of formerly allopatric species of similar size and niche becoming sympatric. Hutchinson provides a relatively famous metric for niche partitioning based on size and features of the “trophic apparatus”, now referred to as “Hutchinson’s ratio”. It simply states that for organisms to exist in the same niche space, they must be separated in size by at least 1:1.3. Remembering his beetles, Hutchinson explains that in their case the size difference is not enough, and C. punctata and C. affinismust therefore separate themselves by breeding season.

Mosiac Nature of Environment
Hutchinson takes a moment here to explore the nature of sympatry in various terrestrial fauna. I think it might be worthwhile to discuss why the interactions seen in various classes are such.
Hutchinson’s closing remarks are largely a re-hash of the points stated above, but in his last paragraph he raises an interesting question- if his assumptions on diversity and community structure are correct, it would mean that great diversity is more obtainable by small species than large; which would in turn mean that the evolutionary effects on each type are different.

A.G. Tansley – The Use and Abuse of Vegetational Concepts and Terms

Arthur G. Tansley writes this article as a response to John Phillips’ papers in the Journal of Ecology, of which Tansley is the editor. Phillips’ papers are about how correct Frederic Clements is about the complex organism and progression to the climax. This article is Tansley’s dissent on Phillips’ and Clements’ uses and the restrictive nature of their vocabulary.

Succession: According to Tansley, succession is not an entirely progressive and developmental process of the complex organism towards climax. Here, Tansley makes the case with retrogressive succession that keeps a heath or grassland from becoming a forest. Heath, by the way, is a type of shrubland. Disturbance is an important aspect in ecology, and Tansley is talking about how destruction is an integral part of some systems.

Quasi-Organism: Tansley speaks often of how he disapproves of the characterization of a community of plants as a “complex organism.” He provides quasi-organism as an alternative, which, apparently, Clements does not budge in compromise with using quasi-organism.

Ecosystem: One of the foundational words we use today is coined in this article: ecosystem. Tansley considered Clements’ initial usage of the word “biome” as much more appropriate than complex organism. Tansley preferred to include the physical factors and the community together as a system. He derives this sense of systems from physics. Tansley focuses on the climate, soil type, and organisms of the system.

The different factors that can affect an ecosystem are the auto- and allogenic factors. Autogenic factors are the processes of the plants themselves; the community of plants drive the change into the different series of succession. Allogenic factors are the abiotic factors of a system, such as soil type and animals within the system.

This is a bit of a long read as far as articles go, but I thoroughly enjoyed reading Tansley’s retort to Frederic Clements and John Phillips. However, Tansley does not completely disagree with Clements and Phillips. He disagrees mainly with their restrictive word usage and definitions.

Tansley equates the holistic nature of Clements’ beliefs on the complex organism with religious fever and dogma. The running commentary on Clements’ position as “prophet” and Phillips’ as “apostle” to the “holistic faith” of succession as a progressive and developmental process for the complex organism towards climax is rather hilarious to me.

MacArthur and Pianka (1966) and Skellam (1951)

On Optimal Use of a Patchy Environment

This work by MacArthur and Pianka is the first to explicitly model the strategies employed by organisms to exploit the resources available in their environment. In modeling these strategies, the authors state, “an activity should be enlarged as log as the resulting gain in time spent per unit food exceeds the loss.” This simple statement sets a straightforward economic rule that guides their treatment of the problem. Through optimization methods they attempt to identify the budgetary components that will increase and/or decrease as certain activities are increased. The authors look specifically at time spent per item eaten and divide that time into search time and prey consumption time (pursuit, capture and eating time).

The economic approach taken by MacArthur and Pianka proves useful in formulating a generalized view of what factors govern foraging behavior. For example, increasing the number of prey species (i.e. moving from a specialist toward a generalist) decreases the amount of time spent searching while increasing the amount of time spent pursuing, capturing and eating prey. This brought forth a number of evolutionary and behavioral implications, which spurred further, more complex research in the field.

The authors employ similar methods in consideration of patchy environments and produce similar results. In short, the addition of patches with equally abundant prey items to a predator’s itinerary, will result in more time hunting and less time traveling per prey item captured. This model should generally hold true for adding patches that have unequal prey densities as well. Moreover, spatial scale influences both travel time between patches and how patches are used but should not change the amount of time spent hunting.

Lastly, competition is addressed. Regarding diet, the introduction of a competitor will reduce the overall abundance of a prey item. Apparently, this will not result in dietary changes. However, the introduction of a competitor will cause an optimal predator to alter its use of a patch. It is also evident that the patch structure of an environment imposes limits on the degree of similarity between competing species. In general, given sufficiently low travel time, a generalist can outcompete and replace a specialist predator. However, a specialist may persist in the presence of a generalist if the hunting rate of the specialist is greater than that of the generalist.

Random Dispersal in Theoretical Populations

In this work, Skellam is the first to utilize the random walk principle in an ecological context. At the time this paper was written, other biologists had used the idea of random walk and diffusion equations to explain the process of genetic drift but had not gone any further than proposing a general model for the spread of an allele through a population. Skellam demonstrates the utility of diffusion equations in approximating many of the key features of population spread. However, he also recognizes that it is a somewhat simplistic model for explaining dispersal as it relates to actual populations, in that populations in the real world do not disperse randomly.

In examining dispersal, Skellam cites the spread of Oak trees and flightless in postglacial Britain. As a slow growing organism with a long generation time and poor dispersal ability, the Oak has spread remarkably fast. To account for this, Skellam posits that there must have been some mediation of dispersal by birds or that the latest glaciation event was less extensive than reported and that refugia persisted. Additionally, he provides some insight into the mechanism of postglacial forest succession as it related to seed dispersal ability. The muskrat, which was introduced to central Europe in 1905, proved a useful case study as its spread following introduction closely matched calculations for a theoretical population.

Skellam also introduced the concept of critical patch size, noting that a particular region of favorable conditions must meet or surpass a certain size threshold in order to support a viable population. Consequently, populations in patches that fall short of this critical threshold should not occur naturally. Skellams work has clearly paved the way for a lot of work that has been done on populations and still has wide ranging implications in the spread of invasive species and conservation biology.

  

May (1974) and Volterra (1926)

Robert M. May (1974): Biological Populations with Nonoverlapping Generations: Stable Points, Stable Cycles and Chaos

In the article Biological Populations with Nonoverlapping Generations: Stable Points, Stable Cycles and Chaos. May explores how dynamical behavior arises from seemingly simple nonlinear difference equations that model population growth as intrinsic growth rate r increases. When using equivalent logistic equation dN/dt = rN(1-N/K) the only long term outcome is stability where N = K (population = carrying capacity). In nature populations fail to reflect this stability. However, when nonlinear difference equations are used, depending on the value for r a spectrum of dynamic behavior can be seen. For the two equations provided (the Ricker map and the logistic map), stable equilibrium similar to that seen in the logistic equation can be achieved for N = K, as long as N > 0 and 2 > r > 0. However as r increases the a series of bifurcations occur resulting stable oscillations between 2, 4, 8, 16, 32… population points until the behavior becomes chaotic at a high enough value of r. This chaotic behavior is however not stochastic, but determinate. So long as the initial population is known the population at any time can be determined using these equations however the slightest differences in starting points can result in drastically different outcomes. From this we can appreciate that the dynamic fluctuations seen in nature may not just be the result of immeasurable interactions and influences but a result of the system itself.

Lorenz is credited as the first to “discover” these chaotic systems in weather modeling. However, his papers presumably had little impact on the scientific community as a whole due the journals they were published in. May can be credited though with bringing these dynamic models into light revolutionizing how dynamic systems are looked at in many fields of study.

Vito Volterra (1926): Fluctuations in the Abundance of a Species considered Mathematically

In this article Volterra explains fluctuations, or abundances in regards to predator prey interactions. He states that one species given enough food would grow exponentially if left to, while a second would perish without food if left alone. However, when the second preys upon the first in a predator prey relationship the two can co-exist in equilibrium. The number of prey decrease as the number of predators increase. As the prey decrease the predators follow this trend leading to an increase in prey. If plotted as population vs time the populations of predators and prey would oscillate out of phase to each other around a specific population size. From this Volterra makes three laws:

I.                   The fluctuations seen are periodic

II.                The average number of predators and prey tend to be a constant as long as nothing else is influencing the system.

III.             If a proportionate number of predators and prey are destroyed, the number of prey tends to increase while the average number of predators decreases. However if the prey are “protected” an increase in the average number of both predator and prey will be seen.

Volterra also goes on to explain two types of associations he has witnessed, conservative and dissipative associations. In conservative associations fluctuations in the numbers of predators and prey appear to remain constant. While in dissipative associations the fluctuations dampen and eventually stabilize.

            It is interesting to note that while Volterra was developing the differential equations to model these predator prey interactions Nicholson and Baily introduced the difference equation modeling parasitoid-host interactions. May argued the latter being applied more successfully.

G. E. Hutchinson, Concluding Remarks and L. C. Cole, The Population Consequences of Life History Phenomena

Concluding Remarks

In this relatively short but dense work, Hutchinson provides a comprehensive, contemporary definition of the niche—effectively uniting Grinnell’s definition of the niche as the distributional unit in which a species is confined, with Elton’s definition of the niche, which focused on interactions with the biotic community. Moreover, Hutchinson does so mathematically by defining the limiting variables that permit a particular species to survive. By considering all biotic and abiotic limiting variables (x₁, x₂,… x_n), the n-dimensional hypervolume (N) or fundamental niche is defined. Hutchinson also identifies some limitations to this model: 1) it is assumed that all points falling within N are equally good and all points falling outside of N are equally bad, 2) it assumes that environmental variables can be linearly ordered, 3) the model represents only a snapshot in time, and 4) only a few species can be considered an once.

Hutchinson examines interspecific competition as the potential overlap of two fundamental niches (N₁ and N₂). In some cases there will be no overlap (i.e., the fundamental niches of the two species will be separate). While in other cases, some points in N₁ will correspond to or overlap with points in N₂. In this case, subsets of points that are unique to one or the other as well as common to both (the intersection subset) can be generated to examine ideas introduced by Volterra and Gause, specifically that of niche specificity.

Hutchinson disregards the possibility of N₁ being equal to N₂, as it is extremely unlikely that any two distinct species will have exactly the same requirements, he also didn’t seem to think that would be very interesting. Instead, he proposes two much more likely cases. In the first case, all of the points within N₂ exist within the space of N₁. In this scenario, competition will favor species 1 and eventually only species 1 survives. Alternatively, competition favors species 2 where it occurs and both species survive. In the second case proposed by Hutchinson, portions of N₁ and N₂ intersect while other portions do not. In this case, some portion of the realized niche will act as a refuge for species 1 and another portion will act as a refuge for species 2 and both will survive. Further, Hutchinson explicitly states that this approach does not validate the “Volterra-Gouse Principle” but provides a starting point for experimental and observational researchers that seek test this principle.

Finally, Hutchinson attempts to address the connection between the realized niche and the rarity or commonness of species in a community. In doing so, Hutchinson relied heavily on theoretical work done by MacArthur and applied concepts of niche specificity to that work. Unfortunately Hutchinson provided relatively little explanation in this section. Perhaps this was a result of the limitations of MacArthur’s model, which was still probably the best model available at the time.

The Population Consequences of Life History Phenomena

At the time of publication, life-history characteristics had received little attention from population ecologists. Cole summarized the diversity of life-history phenomena that influence reproductive potential (fecundity, longevity, age at first reproduction…) and the challenges associated with quantifying their effect on population growth. Moreover, Cole recognizes that each of the countless reproductive strategies must be effective in their respective environments.

On introducing early attempts to explain population growth mathematically, it became clear that some initial attempts were too simplistic or too labor-intensive to be truly useful. Although calculations of population growth may be influenced by noise and calculation problems in short timescales, deeper timescales seem to be free of such issues. Ultimately, Cole concludes that approximation based on a geometric model of growth yield results equivalent to the more labor-intensive methods and are useful in examining life-history evolution as it relates to population growth.

Cole’s calculations indicate that increasing the number of reproductive events (i.e., the evolution of iteroparity) and especially the age at which the first reproductive event occurs, have a strong bearing on population size. However, a paradox identified in this paper suggests it would be more efficient and, for that matter, more likely for a semelparous species to increase it’s litter size than it would be to undergo the numerous evolutionary changes to achieve an iteroparous lifecycle.

On several occasions, Cole reiterates the need to study the evolution of such life-history traits as they relate to populations structure and growth, and biology in general. This mathematical approach provided a framework for many of the population studies that have followed.