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.
Friday, September 30, 2011
Thursday, September 29, 2011
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.
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.
Friday, September 23, 2011
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.
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.
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.
Thursday, September 15, 2011
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.
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.
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.
Friday, September 9, 2011
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.
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.
Thursday, September 8, 2011
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.
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.
Saturday, September 3, 2011
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