Semester Overview and Summation

12/16/11

During the course of this semester I undertook a Directed Readings Seminar with two members of the faculty at the University of North Carolina at Chapel Hill, Dr. James Umbanhowar and Dr. Charles E. Mitchell. Under their guidance and direction I critically examined approximately two sources of literature from the field ecology each week, wrote a combination objective and subjective blog about each, and then met with one of my advisors to discuss the work and its context within the larger field of ecology. The blogs for these papers are given as a transcript below, however the original sources can be found at the website address given in the title of this report.

The range of topics covered stayed mainly within the fields of invasive species ecology and disease ecology, from which both theoretical and experimental studies were analyzed. Generally, the method of article choice for the following week was based on intriguing topics that were embedded in the current weeks’ discussion. We began with an overview of the models used to map the spread of invasive species over time and space. From here we were able to expand into a variety of different types of models used in the ecological sciences, including traveling waves, stratified diffusion, and even the classic SIR models (Susceptible-Infected-Recovered). Several case studies were analyzed for classic epidemiological cases, including measles, Dutch Elm Disease, conjunctivitis of birds in the northeastern United States, influenza, and cholera.

From the experimentally-based literature sources we covered a variety of system choices and pathogen types. Marine diversity of pathogens and their relevance to the resource cycling of primary producers was looked at, along with the interaction of different kinds of pathogens within a single host, whether that host population was buffalo herds from Africa, or bacteria in culture.  We also spent some time investigating the role of lifestyle choices in viruses, whether a lytic (kill first) or lysogenic (incorporate into host genome) lifestyle could be favored in certain environments over others. We ended the semester with an exploration of the role of certain kinds of nutrients on pathogen dynamics.

In summation, a broad range of topics was covered during the course of this seminar. The subject material was chosen from both subfields of invasion and disease ecology. For the most part however, the string of literature sources was woven together from week to week to allow flexibility in topic choice, but also to allow growth and exploration of the more difficult questions presented within the ecological sciences.

Elser, J.J. et al. Ecology Letters. (2007).

"Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine, and terrestrial ecosystems".

Reviewed: 12/02/11

Past research has demonstrated the importance of nitrogen and phosphorus as limiting nutrients across a wide variety of habitats. The relative merits of each one as the primary nutrient of limitation however has been debated across systems. Does the biological demand for N and P depend on the system of study, namely terrestrial, freshwater and marine, or is the biological machinery necessary for photosynthesis and autotroph-ism similarly affected by nutrient additions or limitations across the globe? A meta-analysis comprised of a little more than 1000 separate experiments was the basis for this study. Experiments were drawn from a wide variety of habitat types across each system, and from multiple latitudes. Above- and below-ground experiments were also used for the terrestrial systems. The results of each study was converted into a log-transformed response ratio that looked at the effect of N, P, or N+P addition on some metric of community production or biomass. Only studies that manipulated both N and P within the experimental design were included in the meta-analysis.
Additions of nitrogen and phosphorus were found to induce a significant, non-zero, positive response across all system types. The community response to P additions, averaged across all habitat subtypes, did not significantly differ across the systems, though the response to N alone, as well as to N+P, did differ. Marine systems received a particularly large boost from additions of N relative to P. A synergistic effect was noted from the dual addition of N and P across all systems, whereby the addition of both limiting nutrients induced a larger response than the sum of each nutrient effect alone. The reason for this is unclear and may depend on the habitat of study. Say if for example bacterial autotrophs are better able to cope with reductions in N as long as they are not experiencing a simultaneous decrease in P.
Terrestrial and Freshwater systems were both shown to be equally limited by N and P, though marine was not as previously stated. Within systems however, the equivalence of N and P as limiting nutrients was not always true at the habitat subtype level. Grasslands were shown to be equally limited by both nutrients, while forests responded more readily to phosphorus additions.
The similarities in nutrient limitation across systems highlights the need for researchers to consider both major nutrients in their assessments of ecosystem dynamics, while the variation seen within habitat leads to the notion that every system of study should be assessed independently if possible in order for adequate conclusions to be drawn. The source, cycling, and use of nutrients within each system was not assessed in this article. Though these three dynamics may be of more relative importance within a system depending on the biological demand for a certain quantity, and quality, of nutrients. There are global scale differences in how nutrients are turned over within systems and where they come from. The merits of this study comes from its ability to draw large scale conclusions about community response to key nutrient additions, however a similar broad scale look at the internal dynamics of these nutrients within communities would also be beneficial to future assessments, particularly as they relate to global change.

Cronin, J.P., et al. Ecology Letters. (2010).

"Host physiological phenotype explains pathogen reservoir potential".

Reviewed: 12/02/11

The framework for approaching disease should not always be focused at the level of the individual or population of hosts only. The capacity any given individual has for disease directly affects how that disease might be transferred to other potential hosts within its community. The authors of this study set out to assess the reservoir potential of 6 different grass species, important to California grasslands, as they relate to the the viral pathogen Barley Yellow Dwarf Virus-PAV (BYDV-PAV) and the aphid vector Rhopalosiphum padi. Reservoir potential has been linked to three measurable epidemiological traits: the susceptibility of any given host to transfer of virus by the vector, the competence of the host to pass on the virus to a feeding aphid after the disease had become internally systemic, and lastly the host ability to support vector populations.
During the first Greenhouse experiment physiological traits were measured for the 6 different grasses, independent of viral infection, under conditions of low and high nitrogen. Principal Component Analysis (PCA) was able to condense the results of all five traits down to two primary principal component axes (PC1 and PC2). PC1 explained 50% of the variance in the results and was linked with a beforehand hypothesized continuum of 'Quick-Return' and 'Slow-Return' phenotypes, QR and SR respectively. The QR-SR continuum is a classification continuum that posits that quick growing host species will be more poorly defended but faster growing, while slow return species will allocate less overall to rapid, short-term growth but will have high defenses to enemies. The link of PC1 with this continuum was important for the researchers as it cohesively combined a complex array of traits into one definable axis of study.
Under the two nitrogen regimes, those grasses grown under higher nitrogen were consistently shifted more toward a QR phenotype, except for a single grass host.
The second part of the experiment was designed to test whether the conglomerate of physiological traits was sufficient to explain the three epidemiological parameters previously outlined, or whether host lifespan, phylogeny, and provenance (native vs. exotic) were also required. Nested models were used to test this theory by systematically dropping explanatory terms from the model, and using AIC for comparison. Host physiological traits, in the form of PC1, were consistently necessary to explain host reservoir potential. QR type hosts are more efficient as reservoir hosts, meaning that can support high levels of aphid vectros, become infected more easily, and transfer pathogens more easily. The authors propose host physiology as primary in importance for predicting epidemiological parameters, however the phylogenetic history and lifespan/provenance of a host is still relevant to the discussion, as shown by their significant inclusion in two of the paramters: vector population size and host susceptibility.
This study looked at five traits and ran them through a PCA, however future studies should expand beyond the nitrogen centered framework used here. Multiple nutrients are responsible for the dynamics seen in terrestrial systems, (eg Nitrogen and Phosphorus, as wel as other micronutrients). In this experiment nitrogen was shown to shift grasses towards being a better host. If phosphorus addition or faster growth do to rising CO2 levels do the same, then this will have important implications for global change analysts and the disease emergence in the future. Inclusion of more broad-scope traits would impove the results of this analysis.

Frost, P.C., Ebert, D., & Smith, V.H. Ecology. (2008).

"Responses of a bacterial pathogen to phosphorus limitation of its aquatic invertebrate host'.

Stoichiometric conditions of the food, whether living or not, are known to have strong effects on the organism doing the consuming. Nutrient quality has the potential to drive patterns in enemy fitness as well as to reciprocally affect the tolerance of the host or prey. Three main response variables were analyzed during the source of this experiment, whereby invertebrate Daphnia magna hosts were fed on specific nutrient-levels of food and dosed with a set number of spores of the highly effective microparasite Pasteuria ramosa.  The theoretical queries at the base of this experiment are whether nutrient poor conditions will limit the pathogen as well as the host, or whether under nutrient poor conditions, the pathogen will become preferentially more efficient at utilizing the limited nutrients, thus having a higher virulence on its host.
The infection rate of the bacterial pathogen was assessed by manipulating the nutrient conditions of the food for Daphnia during the infection period, but thereafter maintaining constant nutrient conditions across all treatments, in order to assess only the ability of the pathogen to infect. A linear relationship between phosphorous content of host food and infection rate was observed, with a higher degree of infection for high phosphorus conditions. Based on previous work the authors believe this result if most likely due to reduced growth of the bascterial spores within the host under low phosphorus conditions rather than due to reduced feeding habits or contact rates.
In contrast to the methodology used to assess infection rate, spore production of the pathogen was measured by maintaining constant nutrient conditions of the food during the infection period, but thereafter altering the food content by treatment. In general more spores were found in hosts fed on phosphorus rich foods, though the relationship was non-linear. This pattern seems to be largely driven by the reduced size of the host fed on nutrient poor foods, however the density of spores within the host was non-constant across treatments. this may be interesting for future work as it could help determine if the pattern of reduced spore counts is in fact fully linked with reduced host size. An interaction may exist whereby larger hosts may still contain a proportionately larger number of pathogens in nutrient rich conditions. An experiment with a higher number of treatments, or hosts that are measured for spore containment more often during the potential growth period, could reduce the irregularities within the results and indicate a more concrete relationship between pathogen density and nutrient conditions in this study system.
The final portion of this study hoped to look at the question of nutrient effects on host reproduction. The difference in reproduction at all nutrient conditions was assessed for both infected and uninfected individuals. not surprisingly, infection reduced host production, period. However, this decrease was more dramatic in hosts fed on a low phosphorus diet, indicating that the virulence effects of the pathogen on its host was in fact higher under nutrient-stressed conditions.

Pradeep Ram, A.S. & Sime-Ngando, T. Environmental Microbiology. (2010).

"Resources drive trade-off between viral lifestyles in the planton: evidence from freshwater microbial microcosms"

Reviewed: 11/18/11

The two possible lifestyles of viruses living in aquatic systems has often been considered as a trade-off. Each lifestyle presenting its benefits and costs under certain environmental conditions, and both lifestyles having persisted across evolutionary time because of this very antagonism in the trade-off. The investigators of this paper chose to look at nutrient conditions might be responsible for the conversion of a lytic cycle pathogen into its lysogenic state. They tested the hypothesis that the addition of organic and inorganic nutrients decreases the presence of the lysogenic lifestyle overall. The experiment was conducted in a laboratory setting on samples collected from a freshwater lake in France.
Mytomycin C is an antibiotic that crosslinks DNA and can initiate a repair pathway within cells. It was this chemical that was used to induce prophages to leave the lysogenic stage. This is a common experimental technique used to determine proportion lysogenic stage (based on difference from lytic). More evidence needs to be collected on its accuracy, but overall it seemed to be a better estimating method than the TEM-based counts used as the alternative by the researchers.
Viral abundance increased under conditions where its host was fed on high nutrient based foods. This result was particularly true for inorganic, as well as organic, carbon additions. This increase however, seemed more closely linked to the benefit that the nutrient additions provided to host growth rates and abundance. researchers also measured the burst size, or internal reproduction of the virus within their hosts. The magnitude of this response was again linked to increased carbon conditions.
A ratio of lytic to lysogenic frequencies showed that a higher proportion of lysogens were found in ambient nutrient conditions, as well as for samples that had been experimentally manipulated to contain reduced viral to host ratios. The authors propose that more lysogens can be found in virus-reduced samples because the direct competition between hosts increases, creating poorer host conditions for a potentially lytic-stage virus. This finding was also proposed to be a result of lower contact frequency between host and pathogen stimulating a waiting-type lifestyle (lysogeny), until higher contact rates could be achieved.
Analysis of the Pearson product-moment correlation coefficients shows a very clear antagonism between the frequency of lytic viruses and that of lysogenic ones. This supports the idea that these two lifestyles do in fact represent a distinct trade-off in life-history metrics. The other key result from this experiment, as prevously stated, showed that nutrient additions beyond the ambient increased the proportion of lytic-lifestyle viruses largely as a result of increased bacterial population numbers. However, the study did not demonstrate that decreasing the nutrient conditions within the samples could lead to higher counts of lysogeny. This could be achieved in the future by placing the host species into a broader range of nutrient conditions by chemically manipulating the waters found naturally in the freshwater lake used in this study.

Faruque, S. M. et al. PNAS. (2005).

"Seasonal epidemics of cholera inversely correlate with the prevalence of environmental cholera phages".

Reviewed: 11/10/11

Cholera epidemics in and around Dhaka, Bangladesh occur seasonally, typically twice a year. Cholera is a human disease that is caused by pathogenic strains of Vibrio cholerae, however the bacteria is a normal component of aquatic ecosystems. Aquatic transmission of the disease-causing bacteria was first noted approximately one hundred and fifty years ago and while many environmental and biological parameters have been associated with the temporally varying disease surges, none have been conclusively linked to the cause or finish of the epidemic (other than the obvious requirement of water). Bacteriophages have been shown to be linked to transmission of toxigenicity in V. cholerae. Data obtained from plaque assays, bacterial cultures, and stool samples randomly obtained from local hospitals were used to demonstrate that the abundance of cholera-inducing bacterial strains was inversely correlated with increases in their own pathogens' numbers.
If the presence of a host-specific bacteriophage was noted within any given water sample collected from local aquatic systems, than its target host was significantly less likely to be found. The percentage of co-occurrences of virus and target host was lower than one that could have been predicted by chance alone.
Cross comparison of monthly collection and analysis for phages in water samples with stool samples showing cholera disease in humans did in fact show an oscillating relationship between the bacteria strains and their viruses. Two strains of bacteria are primarily responsible for the disease in Bangladesh, and six phage types, which can actually be grouped into 4 genetically distinct groups, are associated with them. However, the life modes of these viral types are not all homogenous. The authors propose that the difference between lytic and lysogenic capable phages may be driving a lot of the seasonality of the cholera epidemics.
The model they present begins with epidemics arising during periods of low lytic phage concentrations as described above. Modularity in epidemics may come about from the presence of lysogenic bacteria because lysogeny can build up in bacterial populations via prophages. Prophages make the lysogenic bacteria resistant to superinfection by other viral types and concurrently drive other bacterial strain populations down because they do not have the resistance to the lytic-cycle-dominant phages still present in the system.
This difference in lysogeny or lytic cycle phages, if they are capable of only one cycle or both and what would induce them to choose lysis over lysogeny, is here shown to directly link to the basic pattern of the cholera epidemic.
Other studies have provided evidence on the elimination of a previously dominant V. cholerae strain due to differential infection patterns of a lysogenic virus. It seems that more information on what environmental conditions are necessary to induce a lysogenic pathway in the virus would be useful for researchers hoping to identify natural resevoirs for the virus, or inversely the bacteria. Nutritional treatments could easily be applied to cultures of bacteria and virus in a laboratory setting. Similar methods used to test for prophages as were used in this study (methods such as Southern blots and DNA probes) could potentially provide a lot of information on this globally pervasive disease and the bacteria that causes it.

Suttle, C.A. Nature Reviews., (2007).

"Marine viruses - major players in the global ecosystem".

Reviewed: 11/10/11


Viruses are the most abundant player in the oceans when it comes to quantity of genetic information. Viruses in general are a unique class of organisms to consider due to their ability to infect hosts at multiple trophic levels. They have the potential to exert controlling forces on autotrophs and the heterotrophic grazers that feed on them. The author of this review presents information on different aspects of viral ecology as they relate to the specifics of a marine system, as well as the molecular techniques that have been developed to quantify their abundance.
It appears as though marine virus ecology might be fundamentally different from its terrestrial counterpart. It seems to me as if marine viruses can be thought of as existing in one gigantic pool, floating and dispersing after lytic events of their host species. Whereas as in terrestrial systems we usually think about viruses as being vectored or transmitted in some more organized fashion. Along with this idea is a lack of understanding about the way that viruses are transmitted in the ocean. A significant amount of data, involving genetic sampling in many diverse aquatic habitats around the globe, seems to show that their exists hotspots for certain virus families in differential parts of the ocean. It is possible to identify commonalities in different viral lineages as well. The VHSV virus is associated with a disease in trout farms of Europe, but has also been identified in marine fish and in some lakes in North America.
Interest in amassing more data on the specific roles of viruses in the ocean can be linked to their role in turnover and shuttling of carbon and other limiting nutrients as they infect and lyse their hosts. Particulate and dissolved organic matter arising after these events increases the amount of respiration done by decomposers in the photic zone.
The author also presents an interesting analysis of the spectrum of r and K selection in both the host species of the ocean and the viruses that infect them. While it appears that the abundance of marine prokaryotes and eukaryotes is weighted towards k strategists, who are slow growing and resistant to infection, the abundance curves and r-K spectrum is the opposite in the viral community. The most abundant viruses appear to genrally fall under the header of r-selected species, those with rapid replication and generally more virulent. This contrast of host and pathogen has important implications as it indicates that the rarer host species, those that are more r-strategists and are the most susceptible to infection, are controlled by a highly abundant, rapidly-replicating pathogen community. This does not mean that r-selected host species never undergo periods of release where they rapidly reproduce and expand in concentration, but it does imply that viruses are an important control to bring the overall marine ecosystem back to a more equilibrium state. It would be interesting to attempt to quantify both the affect that viruses have on the grazer populations ability to control other dominant eukaryotes or prokaryotes and how viral effects on these lower trophic order species affects the grazers who might feed on them.

Refardt D. ISME Journal. (2011).

"Within-host competition determines reproductive success of temperate bacteriophages".

Reviewed: 11/04/11

Parasites compete with one another just like species in any other trophic level in the ecosystem. This study approached parasitism from this perspective, in a competitor-on-competitor and resource-limited fashion. The results are important ecologically as they suggest that the conceptual tools already present in the field of ecology to think about traditional competition may apply to coinfected parasites within a common host as well.
The study organisms were the ubiquitous E. coli bacteria and 11 bacteriophages. Bacteria are easily cultured and many different molecular and genetic techniques have been previously developed to control their growth in a laboratory setting. Bacteriophages infect by two related but slightly different mechanisms. One involves the lytic cycle, where the parasite hijacks the host machinery to reproduce its own genetic material and then initiates cell rupture and death. The second is the lysogenic cycle, where the phage inserts its DNA into the host DNA, becoming a prophage. The prophage stays internal, and gets replicated with each cell division, until external pressures induce the lytic cycle and cell death. A secondary infection of a prophage into a bacteria already infected with a prophage must be a constant threat, as premature stimulus of the lytic cycle could lower reproductive output for either or both bacteriophages.
This concept shapes the basis of this experiment. Baseline performance of uni-infected prophages was analyzed first in order to note deviations from the normal pattern of growth when coinfected with a secondary prophage. Productivity, phages released per lysed cell, and lysis time, length of the rupture process from moment of externally applied signal, were the primary response variables analyzed.
In the majority of the coinfections, baseline productivity was not maintained by both phages at the same time, indicating a potential hierarchy of competitive ability. Total combined productivity of the two phages was close to 100% in most cases. This lends support to the idea that a high degree of resource/exploitative competition was taking place. However, there was a significant loss of total productivity that could not be explained by the sharing of resources alone, indicating some direct/interference competition between the two coinfected prophages, though the mechanism is not known.
Crucially this study found that coinfection alters productivity of the parasites and that this alteration is differential based on competitive ability. However, coexistence of phages still occurs in nature; one super-phage has not outcompeted all other phages in all settings. This may be because a small proportion of the variance in productivity was explained by the specific combination of the two coinfected phages, and not just a sum of their main effects. The results of this study would also benefit by testing a similar experiment across an array of nutrient conditions and/or host genotype or species. The phages in this experiment resided within a host that was largely not stressed by external environmental factors up until the lytic cycle was induced. An experiment involving staggered pH level differences, or deviations in nutrient content from the idealized petri plate, would be highly applicable to natural world microbial communities, as well as to larger questions about coinfection as drivers in ecological processes.

Jolles, A.E. , Ezenwa, V.O., Etienne, R.S., Turner, W.C., & Olff, H. Ecology. (2008).

"Interactions between macroparasites and microparasites drive infection patterns in free-ranging African buffalo".

Reviewed: 11/04/11

Infection by both macro- and micro- parasites in wild populations occurs at an astonishingly high rate. Among the buffalo herds described in this study the prevalence of Bovine tuberculosis and helminth infection reached as high as 72.5% for TB, and 87.5% for the worms. With such high prevalence of disease it is only natural to assume that some hosts face the double pressure of coinfection; it is also possible to imagine the concept of a herd that contains a higher proportion of coinfected individuals. This study hoped to identify the primary drivers of disease dynamics in buffalo at both the host and host population level.
Worm prevalence in buffalo was found to be highly negatively correlated with TB prevalence. Demographic factors such as sex and age, as well as proportionate amounts of certain individuals within the population, were found to be partial drivers of this pattern. Age plays a role in infection patterns at young ages fro both worms and TB, but males were only slightly less likely to be infected with worms than females.
Researchers also tested the role of a mortality hypothesis in driving disease dynamics using both empirical data and a simple theoretical model to qualitatively assess expected outcomes from different initial conditions. They found that coinfected individuals did indeed have a poorer body condition than those hosts who were singly infected. They also found that individuals with a higher concentration of fecal eggs, a metric of worm pressure, occurred less frequently in TB positive individuals, indicating an increased mortality rate. This pattern was true at the herd level as well based on proportionate amounts of infected individuals. Theoretical work supported a higher mortality rate alone being a partial driver of the negative correlation between worms and TB at the herd level, but only the addition of immunological heterogeneity into the model compared with the disease patterns at the individual level. Individuals infected with worms have a higher immunity to TB infection because of the self-regulation of the immune system in mammals to microparasitic and macroparasitic pathways. The immunity of worm infected individuals to TB reduced the infection rate of TB differentially in the worm-hosts than in regular susceptibles, reducing the total number of coinfections, which have a higher mortality anyways.
This study is interesting because it shows how different kinds of parasites interact and coexist with one another via multiple mechanisms. Differential infection in a shared host population based on sex and age shows niche differentiation, but antagonism between the two parasites was still present in this study. The two immunological pathways within the host reduced the secondary infection of TB when worms were present, though interestingly this was not true in the opposite direction. The result has evolutionary implcations as well, implying that parasites that come in and can reduce their host's risk of infection to a potentially more dangerous potential parasite would have an evolutionary advantage. These same immunological pathways are conserved across mammal species indicating a long occurring pattern of coinfection in the natural world.

Keesing, F., Holt, R.D., & Ostfeld, R.S. Ecology Letters. (2006).

"Effects of species diversity on disease risk".

Reviewed 10/28/11

How does the presence of disease within an organism affect their ability to tolerate and survive? More broadly how does the presence of disease in a population or community of hosts affect the dynamics of that system, and how does the host community affect the pathogen? The study of the movement of disease in a community has long been an area of interest in ecology. The authors of this paper looked specifically at many recent experiments designed to tease out the role of host species diversity on disease prevalence and risk for a community.
Simple mathematical models were used at each stage in their discussion to illustrate how proposed mechanisms could be projectable onto a real ecosystem. For example an increase in the number of non-host species in a community could affect the encounter rate of a host and its pathogen. There are a number of ways it is possible to imagine how this would occur, if a predator induced its prey to move less and hide more, the prey would have fewer interaction events with its disease enemy and so disease risk would be driven down.
This is just one example of 'Encounter Reduction' that the authors propose as a mechanism for how diversity increases could affect the role of disease in a community. However, a rising level of diversity does also have the potential to increase disease risk. This is possible in systems where there is only one host of study, but it is maybe easier to imagine in a system with multiple hosts. If a community already has an abundance of low quality host species, and rising levels of diversity introduce a higher quality host, then the net effect would be one of an amplification of disease within the system.
The concept of a 'dilution host' was introduced by the authors in the later stages of the paper. As I see it this type of host introduction could be thought of as something of a keystone species. Just as we have included the concept of a keystone predator into our ecological vocabulary it is intriguing to think abou the possibility of a keystone disease regulator. A host that, when present in the system, regulates the prevalence of disease across the entire community. This could occur if the species is a poor reservoir for a pathogen, but a high-quality host for a predatory vector. It is unclear how common this type of species is in natural communities, but it is nevertheless an interesting venue to consider for conservation techniques.
The models described relied heavily on the ability to connect a mechanism for diversity effects with the net change in the density of infected hosts. There are other means of tracking disease risk in a system, and these include prevalence of the pathogen and density of infected vectors, among others. One area of future research would be to look at how using any of the various metrics as the measure of disease risk alters the outcome of the net effect of diversity on disease. Though the authors claimed to use the rate of change of infected hosts they often referred to disease risk in the paper simply as disease prevalence. I am wondering if their is a scenario that could arise within a natural system where the prevalence of disease remains high while the future risk of disease begins to decline. This scenario seems possible especially when the dynamics of a disease are primarily caused by non-population level drivers, such as seasonal or environmental. If this is indeed the case, than it seems as though using disease prevalence and disease risk interchangeably may not be the most appropriate. Future empirical or theoretical studies could illuminate this problem.

Seabloom, E.W., Borer, E.T., Mitchell, C.E., & Power, A.G. Ecology. (2010).

"Viral diversity and prevalence gradients in North American Pacific Coast grasslands".

Reviewed 10/27/11

Host-pathogen interactions do not exist inside a bubble, they must be considered within a larger community and geographical context. It is possible to find multiple pathogens coexisting inside a single host species and/or to observe pathogens across a wide range of host species. The authors of this paper collected observational data at 26 separate field sites, analyzing the role of four Barley and Cereal Yellow Dwarf Viruses (B/CYDVs) on three grass host species.  These sites spanned a breadth of close to fifteen degrees latitude and represent a wide range of environmental variables and community composition.
The key questions the researchers hoped to answer revolved around how host identity, environmental attributes, and host community of the site affected pathogen prevalence and diversity. Lastly researchers were interested in the coinfection of pathogens and whether or not that was regulated by the total available pathogen pool at a site. This coinfection can be thought of as synonymous with alpha diversity, a traditional measure of local scale diversity in ecology. Beta diversity in this study was the total possible pool of pathogens at a site, and as these viruses are aphid vectored, the turnover among co-inhabiting host species is of particular importance.
For this study system, prevalence was not found to correlate with coinfection. Statistical analyses of the model variance showed that the two metric of pathogen dynamics could have some minor drivers in common, but that overall the majority of the variation seen likely arises from different primary drivers, ie what primarily causes patterns of coinfection does not cause patterns of prevalence.Prevalence was found to be largely a result of site precipitation and soil nitrate patterns, and coinfection was most correlated with latitude.
Coinfection levels increased significantly the farther north the site occurred, and as global gamma diversity remained largely the same across all sites, this resulted in a net decrease in beta-diversity, lower pathogen turnover. The authors posit that this is the result of a higher density of generalist vectors for the pathogens found in the north. A higher rate of vector herbivory on the grass hosts in general would lead to an increase in the overall transmission of the separate pathogen species and therefore a higher level of coexisting pathogens within a single host. More experimental work would be needed to prove this mechanism however.
It is possible to think of latitude as being a collation of many possible environmental variables (such as sunlight, temperature, etc), however the fact that coinfection was strongly correlated with this metric and not with host species type lends evidence to the idea that maybe the viral coexistence within a single host species is a function of the underlying site-level environmental conditions. Viral coexistence and within host niche allocations is not a traditional example used to think about niche-theory, but if pathogen coexistence is somehow determined by resource availability, mediated by their mutual host of course, this raises many interesting questions for future work, including: what categories of pathogen might be able to coexist with one another (rust, viral, bacterial, etc) based on their differential niches? This sort of question can still apply to viral species such as B/CYDVs.

Cottingham, K.L., Lennon, J.T., & Brown, B.L. Front. in Ecol. and the Envr. (2005).

"Knowing when to draw the line: designing more informative ecological experiments."

Reviewed 10/18/11

Data analysis has at its root the experimental design of a project. Every scientist who hopes to one day analyze their data must begin with the most appropriate arrangement of treatments and replications across space and time. Two alternative methods for analysis are linear regression and analysis of variance (ANOVA), both of which have their pros and cons when it comes to information produced and statistical power of that information.
ANOVA has traditionally been used for discrete independent variables of the presence/absence or type-based, etc. It has also been used for continuous variables that can be grouped into a gradient of categories, such as levels of nutrients in a gradient, or classes of densities in a population. Regression on the other hand is strictly meant for fully continuous independent variables that have a linear relationship with the response. The relationship between the response and independent variables can be transformed, but the basic linear pattern must be met.
ANOVA and linear regression both have at their base the same mathematical model, the general linear model. The difference is that while regression operates to find the parameter estimate for the relationship of the independent and response variable, ANOVA creates dummy variable terms for each level of the discrete independent variable. Then can look at each level and determine whether it differs significantly from the other terms in the model. It becomes readily apparent then that the benefits of using ANOVA come from its power to look at each term separately, without tying to force any sort of pattern onto the relationship between the response and independent variables. The consequences, however, could be a model with an overabundance of terms, leading to a lack of statistical power and making it harder to tie relationships between terms to one another.
Choosing one method over the other for data analysis and experimental design can be easy in some cases: where there is no limit to the number of experimental units for example, or where an independent variable has no underlying continuity to it and so analysis must be done using discrete dummy terms. However, as mentioned before, it is more complicated a decision process when the continuous variable can be grouped.
After reading this review I have decided that there are several main questions to focus on. Limitations on the number of experimental units may require analysis using ANOVA, as more replication ability would be possible. If regression really is the desired method of analysis in this case, than the experiment should be designed so that a fall back of ANOVA is possible. If the relationship comes back as non-linear, than it will also be necessary to use ANOVA. Regression analysis requires that the response and the residuals be normally distributed; this is not as much of a requirement for ANOVA, but accuracy of measurement for the independent variable is critical.
Lastly, the benefits of regression are very simply described but can have huge effects on the quality of research produced. Estimates derived from regression analysis have a much higher statistical power for a lower R-squared value than does ANOVA. Regression is also much better equipped to denote the relationship between a response and independent variable. Those parameter estimates can then be fed into ecological models and used in future research, a great incentive for those running simulation based analyses.

Swinton, J., & Gilligan, C.A. Phil. Trnas. R. SOc. Lond. B (1996).

"Dutch elm disease and the future of the elm in the U.K.: a quantitative analysis".

Reviewed: 10/14/11

Dutch elm disease and its effects on the native populations within Great Britain have been a problem for longer than a century.  Separate epidemics in the last century have been caused by two related strains of fungal pathogen, O. ulmi and O. novo-ulmi. The fungus is shuttled between recently dead trees, that function as a breeding grounds, by a beetle vector. The authors of this article have developed a model to look at the long-term predictions possible from a simple density based characterization of elm trees and their transition to infection and otherwise.
I have already mentioned that recently deceased trees function as a breeding ground for the vector that helps propagate the fungus in the system. However, long-dead trees are no longer able to fulfill this role and this time dependency was accounted for in the model parameters. A couple of assumptions were made by the authors when developing the basics of their model equations. One was that the vector density was proportional to the number of dead trees total based on previous research in the system of study. This allowed for simplification in the equations for overall force of infection.
The model was run against data collected by the National Forestry Commission of Britain in the 1970's, however only non-woodland systems were looked at in order to minimize sampling uncertainty. The most interesting results from their model showed how endemic levels of pathogen could be maintained in the tree populations depending on their lethality and the transmission levels. High lethality, it seems, actually causes a pulse in the system that could allow for recovery of the host tree populations and a lower overall level of endemic infection. This is also related to the reproductive rate of the fungal pathogen, where a low R-naught for the pathogen requires a very high degree of lethality to even reach a point where persistence is possible.
There are however several possible ways in which the model presented was confusing and could be improved. The authors posit that competition between the two fungal species plays a role in the long-term disease dynamics, yet they run their model without the inclusion of the less-aggressive species, O. ulmi, even when it became clear that their was a serious underestimation of infected and dead trees occurring in their model when compared to the data collected by the Forestry Commission. This study would also benefit from a greater amount of information on the life-history traits of both the tree genus and the fungi. The parameter estimates used in their models were chosen using a best-guess method in the absence of data pertaining to the life-history of the trees at juvenile and adult stages and transmission estimates for the pathogens. The fungi themselves have two roles in natural systems, saprophytic and  parasitic; a better understanding of the transition between these two states would be of use in future studies.

Morozov, A.Y., Robin, C., & Franc, A. Jour. of Theor. Biol. (2007)

"A simple model for the dynamics of a host-parasite-hyperparasite interaction".

Reviewed 20/14/11

The study of parasites and their importance in ecological systems is growing. But what about hyperparasites?  There does in fact exist parasites of parasites, and it is possible that their role in systems could be just as important, or more so, than any other tri-trophic interaction. The authors of this paper developed a theoretical model for the study of a host-parasite-hyperparasite interaction using a modification of the classic SIR models for epidemiology. The system they applied their model to includes the former chestnut tree populations in America, the ascomycete fungus C. parasitica, and its parasite Cryphonectrica Hypovirus (CHV). There are several strains of the hyperparasite all which affect the fungus in differing ways. The authors also included a section on the vegetative compatibility of viruses with the fungus. Depending on this interaction the number of infected spores produced by the infected fungus could alter.
The basics of the model centered around 4 different character states for the tree: Susceptible (S), Infected with hyperparasite-free fungus (I), Infected with a hyperparasite-bearing fungus (H), and Dead (R). Infected state individuals can die or be turned into the H state. It is assumed that trees infected with hyperparasite-bearing fungi are able to recover and return to the S state, but a transition to dead (R) was not included, assuming that the reduction of fungal pressure on the tree was so largely reduced by its own parasite that it could not cause death in its tree host.
Two conditions were set for hyperparasite establishment to occur. If both are met, but not one alone, than the establishment success of the hyperparasite, and their corresponding role as a method of biological control, depended next upon the initial conditions. One of these metrics includes a term stating that there needs to be less full recovery of the tree host than their is horizontal transmission of hyperparasite-bearing fungus. A certain number of H state trees need to remain in the system in order to provide new sources of hyperparasite. This also relates to the result that hyperparasite establishment is easier if parasite (the fungus) transmission is high. Evolutionarily I think this result is quite interesting. The high level of reproduction that would typically be considered advantageous for the parasitic fungus becomes less so if it also helps its enemy succeed.
Virulence levels for the hyperparasite also play a role in the model. It would seem that a more mid-line virulence would be more successful for endemic virus levels to persist; too high and fungal pressure on the tree becomes so reduced that the tree is able to recover at a  faster rate than the hyperparasite can propagate itself in the system.
I found this paper to be very intriguing. It is interesting to explore the role of hyperparasites in natural systems, especially as their presence in the chestnut example may be a key reason for why stability of the tree populations in Europe was obtained after an introduction of the fungus while American populations were decimated to the point of extinction. The authors propose that their models could be altered into a framework useable for invasion into a community by transforming the character states of the hosts into states of patch or niche such as; susceptible to invasion (S), invaded (I), invaded by a enemy-controlled invader (H), etc. This is an interesting mental-exercise, but I am not sure of its application ability. The unit of study at the host level is easier as it tends to function more on a live or die scenario. Patches, or niches, are somewhat more complicated as it becomes more necessary to measure the extent of invasion in a patch, and then there is always the question of defining patch boundaries. Either way, it would seem as if the model presented has some merit for study in hyperparasitic systems in the future.

Viboud, C. et al. (2006). Science.

Synchrony, waves, and spatial hierarchies in the spread of influenza.

Reviewed: 10/07/11

There are 3 common subtypes of the influenza virus that have been affecting national health in the USA for 40 years or more; A/H3N2, A/H1N1, and B. The authors of this paper used mortality data for Pheumonia and Influenza over this time range to analyze the inter-state travel and timing of flue epidemics and pandemics. Ranking states by hte size of their populations and noting more intense pandemic years, they found that their is more synchrony in the epidemic as the size of the state population increases and the degree of intensity for the epidemic increases. They also found more synchrony between states that were in total a shorter distance from one another. This synchrony was measured in the correlation of peek epidemic weeks, a metric of timing, and through correlations of death measurements, a metric of intensity.
Mortality was used as a metric rather than cases reported because of the high probability when using number of reported cases that some person was either misdiagnosed with the disease or that the case was never reported at all due to low degree of symptoms. Using mortality does tend to make the predictions for the more severe subtype, A/H3N2, more accurate, but the researchers attempted to correct for sampling error in their measurements, and their results still showed that more intense mortality impacts were associated with the severer subtype and that seasons dominated by this subtype over the past 40yrs had more synchrony over all the states (as measured by standard deviation from the national average) in the timing of the disease.
Some of the most interesting results to come from this study however, are the results linking adult workflows to national and interstate trends in disease spread. Several types of movement were modeled, including air travel, rare long-distance events, and simple Euclidean movements, however when all of these movement types were adjusted for one another, workflow surfaces as the only significant correlation for disease phase and intensity. This is a pretty interesting result as it leads to the notion that adults and workflow dynamics are more important for regional spread of disease, not schoolchildren. Schoolchildren may be responsible for more local aspects of spread, but they do not play as large a role in the national dynamics. Incorporating age structure into other disease systems, including animal, plant, and marine could have similarly fascinating results, and should be investigated in the future. As long as their is some sort of inherent age structure in movements of natural populations, this dynamic should at least be considered as a mechanism for movement, whether or not it turns out to be significant to disease spread models.
This study and the variables used to mark disease trends were generally coarse; they were unable to get down to intra-state disease flows. However, the study was able to efficiently use simple predictors to model synchrony and timing at the national scale. This included results showing how initial disease foci in large states with a high degree of international capitalism, like California, tend to spread disease faster and lead to a higher degree of synchrony nationwide when compared to smaller less connected states such as Wyoming. It can be noted however, that their is a certain capping out effect for large states in the synchrony of events, indicating that higher transmission rates that are an inherent property of the virus itself may be more important than the population size of the initially infected disease focus.

Hess, G.R. et al. (2001).

"Spatial aspects of disease dynamics"
Ch 6 from The Ecology of Wildlife Diseases (ed. Hudson P.J.)

Reviewed Oct 07, 2011

Disease risk at the local or global scale can be modeled in a variety of different ways. Analyses range from historical looking approaches that attempt to recreate using models an observed dynamic in space and time to forward looking approaches that hope to identify key areas for disease prevention action. Metapopulation theory has been of interest to ecologists for a variety of different reasons for several decades now. The basic model describing a series of patches in space that are all equidistant from one another and between which their is some level of immigration and recolonization by members of one patch into another. This dynamical process is believed to prevent large scale, or regional, extinction even if a local population dies out due to stochastic effects.
There are some problems with the classic metapopulation theory that have been addressed by other researchers in recent years, attempting to add in a large dominant patch, where extinction is not possible, or adding environmental variation into the patch quality structure. Work has also been done looking at the application of this theory to epidemiological systems, either modeling hosts themselves as a patch, or modeling a population of hosts as a patch. many of the variables used as inputs into metapopulation models have direct analogues with those for disease systems e.g. an empty patch equates to a susceptible host, extinction of a colony is similar to recovered host or one that died.
Some modifications to the metapopulation model have shown that a metapopulation designed like a wheel and spoke (a central, hub colony connected to outer, spoke colonies - which themselves are not interconnected) have a more consistent reduction in metapopulation extinction than some other spatial orientations including island and stepping-stone. This could have important ramifications for conservationists designing corridors hoping to save host populations without allowing the pathogen levels to also rise.
Many scientists also hope to link this particular spatial framework with landscape epidemiology. Landscape epidemiology has the potential to map disease risk using GIS layers that connect abiotic and biotic inputs to observed presence of pathogen, vector, or hosts. The method for connection of the variables to the disease system can either use statistics to rank the importance of collected factors on disease prevalence or utilize a more mechanistic approach that could link the biology of a pattern with changing conditions. Landscape epidemiology has a lot of important applications but there are some major issues with resolution of the spatial and temporal data due to pixel size from remotely sensed sources or limits to data archiving on those same sources. A balance between spatial accuracy and long-term analyses must be achieved if landscape epidemiology is to have any hope of being relevant for study at more regional or local scales.

Grenfell, B.T., Bjùrnstad, O. N., and Kappey, J.

Traveling waves and spatial hierarchies in measles epidemics

Reviewed 09/29/11

Traveling waves have been predicted for many natural systems driven primarily by some sort of stimulus and impediment; for example host-prey, pathogen-host, invasion, etc. However the empirical evidence for this type of dynamic is traditionally limited due to a lack of adequate spatial and temporal resolution of the ecological process. The patterns of the measles epidemics in England during the latter half of the 20th century provide the kind of data that is necessary for traveling waves analyses.
Annual changes in birthrates, seasonal changes in the transmission of the virus via children attending school, and the large-scale introduction of a vaccine in 1968 make this disease system highly non-stationary however. For this reason, traditional Fourier analysis methods were abandoned in favor of local wavelet power spectrum (LWPS) which is more equipped to handle local changes in periodicity over time and space. Some drawbacks of this method are the requirements of a large amount of data and the inability to handle the analysis of the full time series at once (a restriction in period analysis was required for the three major British cities phase angle analysis).
The authors present results showing that two peaks, a roughly annual and biannual, of disease could be discerned for England and Wales during the pre-vaccination era. After the introduction on the vaccine we see a gradual increase in the periodicity of the longer-term epidemic, formerly biannual, that coincides with a steady rise in immune potential hosts. The most intriguing dynamics result from a closer analysis of the spatial patterns of disease centered around the density of human population centers, ie cities verses rural disease dynamics. There is a clear increase in the phase differences between population centers the farther a town is from London or Manchester, two major cities located in the east and northwest of the country, respectively. A smaller, yet still significant, trend can be shown for increasing population sizes as well; as the city gets larger the phase difference between it and the major urban centers decreases.
These facts lead to some important conclusions, mainly that small towns tend to have a lag in epidemic because they are not large enough for the disease to remain endemic on a seasonal or yearly basis, they require an infection spark to drive high incidence levels of the disease. These sparks are brought in from nearby large cities which themselves are still prone to periodicity in the disease dynamics. Analysis of multiple cities, including London, Manchester, Norwich, and Cambridge, shows that the closer larger cities are to one another the more in sync the phase angles of their disease epidemics are. The biennial disease epidemic in Norwich, which is relatively more isolated, was close to a year off of the disease peaks in both Cambridge and London, which had similar phase alignment.
The phase difference between Manchester and its surrounding suburbs was not as clear of a trend as that for London and its surrounding towns, leading to the conclusion that the presence of multiple large urban centers in a small section of the country complicates disease dynamics in that area. Small towns around Manchester were influenced  by the disease dynamics in this urban hub, but also received signals from other urban centers in the area such as Liverpool and Sheffield. These trends of disease resurgence in small population centers via spark introductions from large population centers has a strangely rescue-effect-like mentality borrowed from colonization-extinction dynamics in metapopulation studies. The ability to handle non-stationarity in temporal dynamics and environmental heterogeneity of host populations may have increasing value to ecological modeling in the future.

Ostfeld, R.S., Glass, G.E., and Keesing, F.

Spatial epidemiology: an emerging (or re-emerging) discipline

Reviewed 09/29/11

Spatiotemporal patterns are at the base of many ecological studies. These patterns can be modeled based on incidence, spread, or prediction. They can also be analyzed through a mechanism based framework or one that looks more at large scale biogeographical processes.  The use of spatial modeling in epidemiology has many facets itself. There are many different kinds of diseases and likewise many modes of transmission, which can affect how a pathogen is examined. Diseases that are passed via ingestion of some kind are fundamentally different from those that can jump hosts at small spatial scales.
Models can be either spatially explicit or implicit. Implicit models look at space from relativistic perspective, the where itself does not matter as long as there is a where. Metapopulation analysis is one example as is wave spread of emerging diseases in previously uninfected and non-resistant populations.
Explicit analysis on the other hand tries to combine the spatial and temporal dynamics of the vector (if one exists), the reservoir hosts (if we are only interested in a terminal or primary infected populations such as humans or economically important species), and pathogen incidence itself in focal host or hosts. Forward looking models are often used to map areas and time-scales of high and low ecological risk. When modeling human diseases unfortunately though it does not seem as if taking a purely ecological perspective is the best however. Fundamental to the definition of ecological risk is the concept of exposure in the absence of preventative measures; which many human populations take steps to avoid. This can include such simple measures as bed netting for mosquitoes in areas with traditionally high malaria rates to vaccines for entire populations.
The authors of this paper argue in many subtle and not as subtle ways for the use of mechanisms in linking underlying spatiotemporal distributions with disease incidence. It is not enough to correlate a high percentage of wetlands with mosquito abundance if no information is available on how frequently mosquitoes in the area are infected with the virus in general. This is just one example given in a table displaying similar lines of thinking.
There are of course many difficulties that can arise from this type of modeling perspective. For one it is possible to have multiple vectors or hosts/reservoirs, each with their own unique spatial patterns. One example is given of how the life stages of a vector can drastically affect how tick-borne encephalitis is transmitted, and those specific requirements are difficult to synthesize using spatial models alone. Landscape level conditions can have profound impacts beyond just the inclusion of density or abundance spatial models. Patchy habitats and the interspace between those "ideal" spots can alter how disease incidence cycles and moves at the regional or global perspective. Emerging diseases are also difficult to model as they can be either completely new to a population through introduction or new in terms of their accelerating degree of infection through some sort of ecological release.

Higgins, S.I., Richardson, D.M., and Cowling, R.M. 1996. Ecology.

Modeling invasive plant spread: The role of plant-environment interactions and model structure.

Reviewed: 09/23/11

The modeling of invasive spread has a long history in ecology. However the accuracy and ecological relevance of many models developed to predict this spread have often been unrealistic in terms of analytical input or accurate outputs. This paper attempts a comparison between the classic reaction-diffusion (R-D) models of invasive spread and the spatially-explicit, individually-based (SEIB) variety that the authors develop here. RD models are simple, requiring only population growth rate and diffusivity. SEIB models have the benefit of incorporating age structure, stochasticity, environmental heterogeneity, and various plant attributes into the model. SEIB models are also more equipped to look at spread patterns and not just rates.
The authors look at the invasion of South African fynbos communities by pine tree species by developing a 2-D modeling space composed of 100m2 grids arrayed in a 150x400 pattern. Pine species begin along one edge of the environment, representative of a Pine-tree plantation. The model is set up so that only one tree can exist inside a grid at a time, which is supposed to represent the natural canopy cover of the species; however the modelers include a simulation experiment looking at the size of these grids and how it affects the success of the model as a test of sensitivity. The authors used several factors to determine how individuals would spread in the SEIB model, including adult survival after fires, fecundity, dispersal distance, age at reproductive maturity, and time between fires. These same factors and there assumptions were used to calculate values for population growth and diffusivity in the R-D model, though none able to be explicitly incorporated into that simple model. These five factors were crossed in a factorial simulation experiemnt to give 32 factor combinations, replicated 10 times.
Both the R-D and SEIB models predicted similar mean rates of spread, though the range was much larger for the SEIB model. This makes sense as SEIB is designed to incorporate stochasticity into the model. Both models also found that adult survival after fires was not important to any of the response variables tested, indicating that because the models were designed to only allow for new or open territory to be invaded, the amount of "virgin" territory far outpaces the rate of new grids recycled back into the system after adult mortality. The SEIB model was also able to test for interactions between various factors. For example, the simulations showed that a short fire interval increased recruitment for fast maturing populations, but caused mortality for slow maturing ones. These ecologically relevant facts would have been undetectable using the R-D model. This leads to one of the key benefits of this model according to the authors, the ability to determine which factors in a community are the most relevant for study and further empirical investigation. Different factors were also shown to have varying degrees of relevance for different response variables such as bare mean spread or density of the invasion focus.
The model does come with certain caveats however. The mean distance of dispersal was modeled as a negative exponential model that was cut off after 1km distance from parent. This ignores any potential for long distance dispersal events. Model improvements could easily be obtained by feeding in better data on dispersal distributions for the pine trees or whatever invasion is being modeled. The size of the grid used for the experiment is also vary important. For this system, the tree canopy acts as the ecologically relevant factor determining grain size and the total number of grids tested was 60,000 (150x400) due to size limitations in the simulation. Results showed that the grid size did alter the outcome of all or most of the response variables and the importance of the factors in the invasion spread; though the authors did not test any grid sizes smaller than their main grid of 100m2, they did test several higher ranges. It is of note that if the size of the grid were to be drastically reduced in order to be more relevant for smaller invader species, say grasses or fungi, limitations in the ability of the model to handle a high number of grids may be limited. Meaning that for smaller species, the SEIB model may not be able to look at a very large swath of total landscape, reducing the usefulness of the model.

Levine, J.M. 2011. Science.

Species diversity and biological invasions: Relating local process to community pattern.

Reviewed: 09/23/11

The question of species diversity and its effect on the invasibility of communities is confounding and not yet fully determined. This paper attempts to show some of the finer points of this complexity using a natural-system based approach to looking at diversity and invasion. The set-up is a riparian system in California where tussocks form from the common sedge Carex nudata. These tussocks form miniature islands of varying size that provide structure and habitat for native and invasive species. The author saw that tussocks with higher diversity had an increasing likelihood of the presence of three invasive test species. This observational test was performed on tussocks of similar size. The study does not include counts of invaders present, simply presence.
The next experiment was designed to determine if the observed pattern was simply a function of invaders desiring similar environmental conditions as the natives, or if diversity itself was the cause. All species were removed from test tussocks and replanted with a range of 1 to 9 native species out of a pool of 9 species. After a primary growth season, seed of all the invaders was added to each tussock. The results showed that species richness had an effect on invader success. The r-squared values were low however and all invaders were seeded together on each island, ignoring possible confounding effects of invader-invader interactions.
The author provided one further test to account for the covariance of several variables, such as percent shading, disturbance, and number of propagules via downstream delivery of the river. Richness was only able to account for 25% of the explained variation in invader success. The final experiment showed that invasion success was not altered by richness if counts of initial invader propagules was high.
Taken together these results illustrate a key point: that species diversity has an important alleviating effect on invader success at the local/neighborhood scale, which was easily defined by the boundaries of the tussocks. However, at the community scale, the same processes that drive high diversity, namely downstream delivery of seeds, or covary with high diversity, may be responsible for driving the establishment and survival success of invaders upwards. Leading to the main conclusions that more diverse communities may be more invasible, but a decrease in diversity, particularly at the local scale, may also allow invader success.

Shigesada N., Kawasaki K., and Takeda Y. (1995). The American Society of Naturalists.

Modeling Stratified Diffusion in Biological Invasions.

Reviewed 09/16/11

In keeping with the authors way of laying out their paper, I will review this paper in a 'header with sub-headers and points' fashion. If we envision that invasion can occur by two basic types of dispersal, short-distance (SD) and long-distance (LD), how do we model each dispersal contribution over time, in conjunction with its relationship to the other dispersal type?
The authors detail four examples of invaders and their range expansion over time. The selected species represent a range of continents for invasion, species type, and method of introduction and dispersal.
1) A small mammal (muskrat) invasion shown to have a linear increase in area invaded over time. Dispersal mostly SD. Invasion occurred from a single focal point.
2) A bird (starling) with a bi-linear, or biphasic invasion. Invasion initially occurred from a single focal point, with scattered overwintering birds later providing important points for range expansion.
3) A beetle (weevil) with a completely non-linear, accelerating expansion of range invaded over time. Typical dispersal is by short distance swimming through rice paddies paired with rare long distance flying events.
4) A grass (Bromus) with a non-linear, accelerating expansion over time. Had multiple early foci due to human-mediated dispersal that ended in the late 1800's.
The authors qualitatively classified invasion into three temporal phases: an establishment phase with low density and/or population count levels, an expansion phase of area invaded, and a saturation phase. The establishment phase noted in the examples could be due to two possibilities, the difficulty for establishing populations at low densities or a difficulty for human detection of invader populations at low densities. The Expansion Phase was further classified by the authors as either linear (Type 1), biphasic (Type 2), or accelerating (Type 3). All of which had matches with the selected example species.
Three main model types were detailed by the authors, mostly focusing on the expansion phase of the invasion for simplification purposes. A homogenous environment and a radial spread of the invasion front for each focal colony was assumed. Interactions with native species and patchy habitats were ignored. The three basic models discussed were:
One based on SD dispersal and establishment time. The second on scattered colonies, where multiple foci of colonies are apparent at the start of the invasion, with little radial overlap of the invasion front stemming from each colony. The third and final model looked at a primary colony that created secondary colonies via LD dispersal, where there was significant coalescence of smaller growing secondary colonies with the, also expanding, primary colony of invaders.
The authors did a fairly decen tjob of outlining their models in a very structured fashion. After the outline and boundary conditions of each model was given, they were fit to preexisting data from the example species, outputting fitted terms for the empirical sets. Importantly the authors also detailed colonization success of LD- propagules. For their models they input three basic ways to look at colonizer success rates. The rate could equal some constant value for all colonies and all LD dispersal events. Or a linear increase with the radius of the colony is possible, this assumes that LD dispersal only occurs at the edges of any given colony. Finally a quadratic increase of LD dispersal success by the radius of the originating colony; this assumes that the number of LD migrants is proportional across all areas of the originating colony. All three had their equivalents in the example species. However the authors also point out that the colonization success is not necessarily limited to these three examples. A Type 3, "Accelerating Invader Expansion", response can be achieved as long as the success of new colony establishment from LD dispersal events is greater than linear.
I believe that the patch dynamics of the environment, biotic interactions of the invader, and the differential colony growth at the expense of density increases would be interesting future aspects to analyze within this modeling framework.
On a slightly off-course train of thought I was also left thinking how invasion and disease emergence are similar and different in their modeling approaches.

Davis M.A., Grime J.P., Thompson K. (2000). Journal of Ecology.

Fluctuating resources in plant communities: a general theory of invasibility.

Reviewed 09/15/11

This paper details a conceptual theory of the invasibility of  plant communities. This theory is usually referred to as the Fluctuating Resource Availability hypothesis by the authors, I will simply refer to it as FRA. Invasion is thought to occur when the characteristics of the potential invader are adequate, their is a rise in the number of invading propagules, and the environment is particularly prone to the invasion. The theory supports that the invasibility of a community is directly linked to episodic rises in the local availability of unused resources. This can occur by two primary mechanisms; use by residents decreases for whatever reason, or resource supply increases. The fluctuation of the resource highs is key to the theory, and it must coincide with a high count of invading propagules to yield an invasion.
The authors present evidence from a variety of other studies on how previous sub-mechanisms both fall under the category of their hypothesis for invasion and provides support for it. Disturbance, whether local or regional, can lead to outright increase in resources in the local environment or it can decrease the use ability of residents and/or increase resident mortality; all of which can lead to a higher degree of resource availability. Eutrophication and floods are also examples of increases in resources for plant communities, as well as smaller events that lead to a rise in available sunlight for the invaders.
It is also suggested that the disequilibrium in optimal invasion conditions can lead to a diequilibrium in the interspecific competition rates between residents and invaders, which can in turn lead to coexistence of the two species. To me this leads to an idea that their may be some optimal range of high to low resource availability (in time or quantity) that can lead to coexistence, and that this ratio could potentially be modeled. It also seems to fit with theories about roadside invasions, which is a habitat that experiences frequent disturbance; possibly too much for native species to coexist with invaders.
While propagule intensity was posited by the authors as crucial to their theory, they provided little discussion on the variable. I also had some issues with the seven "predictions' they detailed in the final sections of their paper. It seemed as if predictions 2-5 were just examples of their theory and not really predictions in and of themselves.
Prediction 6 also registered as problematic to me. "There will be no necessary relationship between the species diversity of a plant community and its susceptibility to invasion."They argue that species diversity is not a consistent predictor of invasibility, but it is possible that relative high diversity may still negate a communities' invasibility, via the mechanism that by probability alone, at least a few species in a more highly diversified community will be able to pick up the slack and use more resources as they become available. I am not convinced that the mechanisms behind this particular prediction have been fully explored in their paper.

Hosseini P.R., Dhondt A.A., and Dobson A.P. (2006). Ecology.

Spatial spread of an emerging infectious disease: Conjunctivitis in House Finches.

Reviewed 09/09/10

The authors of this paper present work conducted on the emergence of an introduced pathogen in House Finch populations. Mycoplasma gallisepticum (MG) is a bacteria that produces conjunctivitis in house finches,the infection symptoms in the birds were first noted in 1993-1994. Uniquely the host population also represents a long term invasive in the continental US; house finches were traditionally only found in the Western US until a point introduction in New York around 1940. Bird invasion is fundamentally different from animal invasive studies because their seasonal migration patterns violate the requirements of diffusion theory, which can be used to model invasion. Except at extremely high prevalence of MG in the house finch population, the pathogen attacks only one focal host.
The data was collected by amateur bird watchers who noted presence-absence of both infected and uninfected finches at their backyard birdfeeders. This data was than aggregated to county wide 'grid cells' across states up and down the Eastern US.
A logistic regression model was fit with parameters for observer effort, land use, regional presence of disease in recent past, and host migration.Prevalence of disease was calculated from a days observed perspective and not from total observations of finches so as to reduce inaccuracy of observer efforts.
The data showed an initially linear increase in the spread of the disease, consistent with diffusion theory, but then began to cap out, conceivably as the optimal novel environments for the disease to infect its host dwindled. This result is posited as a condition for diffusion theory to be applicable: that the invader (MG in this case) can not experience an Allee effect due to initial low abundance, and that the linear trend will continue as long as their is a high degree of optimal new territory in which the spread can occur. For MG the decrease in the velocity of the spread may be due to a decline in human dense-suburban environments, where their host, the house finch, is itself most commonly found. The disease was marginalized at the edges of its host population.
The house finch invasion was however most limited by its initially small population after introduction and has only more recently begun to accelerate its spread.
The data available for seasonal trends in the disease spread was perhaps the most interesting part of the article however. Deviance from the predicted trend showed that peak prevalence in disease occurred in the summer breeding season up to October, the time when finches come together for southward migration, with a smaller peak in prevalence noted in early spring, a time when the bird population is also experiencing some regional dispersal movements.
The complex nature of the seasonal movement of the birds is matched by the complexity of the spread rates f the disease over the year. Time lags due to regional prevalence do not fit into simply definable categories. Rising disease trends in one part of the year had effects on later months, and were themselves affected by earlier trends in the year. For example, the month of July had low overall disease prevalence, but was at the start of a rising seasonal trend in prevalence.This is linked with the key early dispersal of juvenile finches.
The biggest query I had with the authors methods was their reliance on data to model disease prevalence that was based on the fact that symptoms were easily observable and traceable in the host population. However, at the end of the paper they draw key conclusions as to the regard of asymptomatic individuals that may be driving future disease spread even in non-peak seasons of the disease. This they say is the reason why seasonal trends in general were important in their model, but no single month alone was deemed critical.

Hastings A. et al. (2005). Ecology Letters.

"The spatial spread of invasions: new developments in theory and evidence."

Reviewed 09/08/11

Hastings et al. begin with a historical perspective on spread models, particularly the uni-spatial model developed by Fisher (1937). In this model the rate of species spreading over time is related to the sum of population growth in that spot/time and the displacement/relocation to that spot. The model assumes a number of important factors, among them are environmental homogeneity and that the growth rate of the population is high when the density is low. Mean age and variance for species reproduction and dispersal are also not directly incorporated into model parameters. Many newer models have been developed over the years that attempt to address one or more of these problems.  
It seems that one of the big limitations in modern theoretical understandings of invasive spread is the lack of data on long-distance dispersal events. Does the probability distribution for dispersal resemble a normal curve, or is a distribution with a tail necessary for modeling? This questions also addresses how invasion is thought of, in terms of being a multi-point origination of invasion due to rare long distance establishment events, or does invasion move as a front across landscapes.
The authors present regression models as an alternative to parametrizing life history models (pop. growth rate and Dispersal). Some of the benefits of regression models are their ability to give Confidence Intervals for invasion spread in space and time, assuming studies report standard errors, and that generalized linear models can be used. They do however require a lot of data in order to make accurate future predictions.
One of the things that I found intriguing about this article was the seeming lack of agreement across studies about whether invasion should be conceptually/literally modeled as stochastic, oportunistic, or deterministic. Does invasion occur randomly, do species respond to environmental and biotic stimuli that open up new opportunities, or do they actively seek or avoid certain habitats? It is of course possible that this framework is unique to every species or life-history type. This question was not headlined by the authors, though they did make reference to the different types in their paper.
I found the section on evolution in invasives also very interesting. Is phenotypic plasticity, or the ability to acclimate to a large variety of environments, a necessary trait for an invasive, or does local adaptation also play a key role after those medium to long range dispersal events? I think this type of thinking is analogous to other theoretical arguments in global change ecology. Though I suppose that invasion, or the switch to invasion, may become more and more linked with global change in the future. Species that are introduced in a new area and those that are facing environmental pressures to expand their ranges share many theoretical questions in common.

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