Achieving food security will require closing yield gaps in many regions, including Pakistan. Although fertilizer subsidies have facilitated increased nitrogen (N) application rates, many staple crop yields have yet to reach their maximum potential. Considering that current animal manure and human excreta (bio-supply) recycling rates are low, there is substantial potential to increase the reuse of nutrients in bio-supply. We quantified 2010 crop N, phosphorus (P), and potassium (K) needs along with bio-supply nutrient availability for Pakistani districts, and compared these values to synthetic fertilizer use and costs. We found that synthetic fertilizer use combined with low bio-supply recycling resulted in a substantial gap between nutrient supply and P and K crop needs, which would cost 3 billion USD to fill with synthetic fertilizers. If all bio-supply was recycled, it could eliminate K synthetic fertilizer needs and decrease N synthetic fertilizer needs to 43% of what was purchased in 2010. Under a full recycling scenario, farmers would still require an additional 0.28 million tons of synthetic P fertilizers, costing 2.77 billion USD. However, it may not be prohibitively expensive to correct P deficiencies. Pakistan already spends this amount of money on fertilizers. If funds used for synthetic N were reallocated to synthetic P purchases in a full bio-supply recycling scenario, crop needs could be met. Most recycling could happen within districts, with only 6% of bio-supply requiring between-district transport when optimized to meet national N crop needs. Increased recycling in Pakistan could be a viable way to decrease yield gaps.
The population size has far-reaching effects on the fitness of the population, that, in its turn influences the population extinction or persistence. Understanding the density- and age-dependent factors will facilitate more accurate predictions about the population dynamics and its asymptotic behaviour. In this paper, we develop a rigourous mathematical analysis to study positive and negative effects of increased population density in the classical nonlinear age-structured population model introduced by Gurtin \& MacCamy in the late 1970s. One of our main results expresses the global stability of the system in terms of the newborn function only. We also derive the existence of a threshold population size implying the population extinction, which is well-known in population dynamics as an Allee effect.
Globalization has increased the potential for the introduction and spread of novel pathogens over large spatial scales necessitating continental-scale disease models to guide emergency preparedness. Livestock disease spread models, such as those for the 2001 foot-and-mouth disease (FMD) epidemic in the United Kingdom, represent some of the best case studies of large-scale disease spread. However, generalization of these models to explore disease outcomes in other systems, such as the United Statess cattle industry, has been hampered by differences in system size and complexity and the absence of suitable livestock movement data. Here, a unique database of US cattle shipments allows estimation of synthetic movement networks that inform a near-continental scale disease model of a potential FMD-like (i.e., rapidly spreading) epidemic in US cattle. The largest epidemics may affect over one-third of the US and 120,000 cattle premises, but cattle movement restrictions from infected counties, as opposed to national movement moratoriums, are found to effectively contain outbreaks. Slow detection or weak compliance may necessitate more severe state-level bans for similar control. Such results highlight the role of large-scale disease models in emergency preparedness, particularly for systems lacking comprehensive movement and outbreak data, and the need to rapidly implement multi-scale contingency plans during a potential US outbreak.
The non-specific lipid transfer proteins (nsLTP) are unique to land plants. The nsLTPs are characterized by a compact structure with a central hydrophobic cavity and can be classified to different types based on sequence similarity, intron position or spacing between the cysteine residues. The type G nsLTPs (LTPGs) have a GPI-anchor in the C-terminal region which attaches the protein to the exterior side of the plasma membrane. The function of these proteins, which are encoded by large gene families, has not been systematically investigated so far. In this study we have explored microarray data to investigate the expression pattern of the LTPGs in Arabidopsis and rice. We identified that the LTPG genes in each plant can be arranged in three expression modules with significant coexpression within the modules. According to expression patterns and module sizes, the Arabidopsis module AtI is functionally equivalent to the rice module OsI, AtII corresponds to OsII and AtIII is functionally comparable to OsIII. Starting from modules AtI, AtII and AtIII we generated extended networks with Arabidopsis genes coexpressed with the modules. Gene ontology analyses of the obtained networks suggest roles for LTPGs in the synthesis or deposition of cuticular waxes, suberin and sporopollenin. The AtI-module is primarily involved with cuticular wax, the AtII-module with suberin and the AtIII-module with sporopollenin. Further transcript analysis revealed that several transcript forms exist for several of the LTPG genes in both Arabidopsis and rice. The data suggests that the GPI-anchor attachment and localization of LTPGs may be controlled to some extent by alternative splicing.
Multiple viruses are widely studied because of their negative effect on the health of host as well as on whole population. The dynamics of coinfection are important in this case. We formulated an susceptible infected recovered (SIR) model that describes the coinfection of the two viral strains in a single host population with an addition of limited growth of susceptible in terms of carrying capacity. The model describes five classes of a population: susceptible, infected by first virus, infected by second virus, infected by both viruses, and completely immune class. We proved that for any set of parameter values, there exists a globally stable equilibrium point. This guarantees that the disease always persists in the population with a deeper connection between the intensity of infection and carrying capacity of population. Increase in resources in terms of carrying capacity promotes the risk of infection, which may lead to destabilization of the population.
An SIR model with the coinfection of the two infectious agents in a single host population is considered. The model includes the environmental carry capacity in each class of population. A special case of this model is analyzed, and several threshold conditions are obtained, which describes the establishment of diseases in the population. We prove that, for small carrying capacity K, there exists a globally stable disease-free equilibrium point. Furthermore, we establish the continuity of the transition dynamics of the stable equilibrium point, that is, we prove that, (1) for small values of K, there exists a unique globally stable equilibrium point, and (b) it moves continuously as K is growing (while its face type may change). This indicates that the carrying capacity is the crucial parameter and an increase in resources in terms of carrying capacity promotes the risk of infection.
How species respond to changes in environmental variability has been shown for single species, but the question remains whether these results are transferable to species when incorporated in ecological communities. Here, we address this issue by analysing the same species exposed to a range of environmental variabilities when (i) isolated or (ii) embedded in a food web. We find that all species in food webs exposed to temporally uncorrelated environments (white noise) show the same type of dynamics as isolated species, whereas species in food webs exposed to positively autocorrelated environments (red noise) can respond completely differently compared with isolated species. This is owing to species following their equilibrium densities in a positively autocorrelated environment that in turn enables species species interactions to come into play. Our results give new insights into species response to environmental variation. They especially highlight the importance of considering both species interactions and environmental autocorrelation when studying population dynamics in a fluctuating environment.
In times when climate change is expected to cause an increased environmental variability it is important to understand how species respond to disturbances. We explore how the stability of species respond to changes in environmental noise by introducing noise colour to different spatial and multi-trophic model systems: (1) a diamond shaped food web with stable oscillations, (2) a stabilized diamond shaped food web, and (3) a food web with stable dynamics. We conclude that adding space and additional trophic levels makes species response to environmental noise colour consistent. All three food webs and species decreased in stability with increased redness, positive temporal autocorrelation, of the environmental noise. Hence, interactions between noise colour and species responsiveness previously found in single- and multi-species models were not found when comparing more natural food webs differing in stability properties. When adding a spatial dimension, all food webs and species increased in stability. Both the diamond shaped and the stabilized diamond shaped food web were significantly more stable than the food web with more typical stable dynamics when existing in a variable and spatial setting. The major route to explain stability and the existence of a diverse world may then be the variable and spatial complexity of nature.
The transportation of animals to slaughterhouses is a major welfare concern. The number of slaughterhouses has decreased over time in Europe due to centralisation. This is expected to increase transport time for animals and as a consequence negatively affect animal welfare. We propose an optimisation model based on a facility location model to perform strategic analysis to improve transportation logistics. The model is tested on the Swedish slaughter transport system. We show that, by strategic planning and redirection of transports while keeping the slaughterhouse capacities as of the originaldata, the potential exists to reduce transport distance by 25% for pigs and 40% for cattle. Furthermore, we demonstrated that approximately 50% of Swedish slaughterhouses can be shut down with a minimal effect on total transport distances. This implies that in terms of the overall welfare picture, the decision of which animals to send where plays a for more significant role than the number of slaughterhouses. In addition, by changing relative weights on distances in the optimisation function the amount of individualtransports with longjourney times can be decreased. We also show results from altered slaughterhouse capacity and geographical location of slaughterhouses. This is the first time an entire country has been analysed in great detail with respect to the location, capacity and number of slaughterhouses. The focus is mainly on the analysis of unique and detailed information of actual animal transports in Sweden and a demonstration of the potential impact redirection of the transports and/ or altering of slaughterhouses can have on animal welfare.
The number of slaughterhouses in Sweden has decreased over time. Fewer slaughterhouses are expected to affect the transport time for animals and as a consequence animal welfare. We have analyzed the transportto- slaughter system, for pigs and cattle, using transport-data from 2008, geographical information for slaughterhouses and farms, and actual route distances between facilities. We made a strategic analysis of the existing slaughterhouses and tested the impact of numbers decreasing further.
With strategic planning the potential reduction of transport distance is 25% for pigs and 40% for cattle. About 50% of the slaughterhouses in Sweden could be closed down with small effect on the total transport work. This implies that for the national total animal welfare which animals are sent where, is much more important than the number of slaughterhouses. However for the welfare (transport time and distance) of the animals in long transports number of slaughterhouses (regular or mobile) is important. Animal welfare weights of distances in the objective function decreases the amount of transports with long route times. We have investigated where in Sweden it would be beneficial to use mobile slaughterhouses. Animals are usually not sent to closest slaughterhouse; we show how slaughterhouse capacity must change if that transport strategy was applied.
Animals are more stressed on long transport routes with stops at many farms. The positions of farms and abattoirs are the basic properties that set the limits for route planning. Mobile abattoirs can reduce the cost of transportation and increase the welfare for the animals. The trade-offs between welfare and profit can be reduced by effective route planning. We have, by computer simulations, investigated how trade-offs differs between areas in Sweden and in general landscapes. The general results are applicable to any area and hence for animal transportation in general.
Theoretical exploration of network structure significance requires a range of different networks for comparison. Here, we present a new method to construct networks in a spatial setting that uses spectral methods in combination with a probability distribution function. Nearly all previous algorithms for network construction have assumed randomized distribution of links or a distribution dependent on the degree of the nodes. We relax those assumptions. Our algorithm is capable of creating spectral networks along a gradient from random to highly clustered or diverse networks. Number of nodes and link density are specified from start and the structure is tuned by three parameters (gamma, sigma, kappa). The structure is measured by fragmentation, degree assortativity, clustering and group betweenness of the networks. The parameter gamma regulates the aggregation in the spatial node pattern and sigma and kappa regulates the probability of link forming.
We consider a system of nonlinear partial differential equations that describes an age-structured population living in changing environment on $N$ patches. We prove existence and uniqueness of solution and analyze large time behavior of the system in time-independent case and for periodically changing environment. Under the assumption that every patch can be reached from every other patch, directly or through several intermediary patches, and that net reproductive operator has spectral radius larger than one, we prove that population is persistent on all patches. If the spectral radius is less or equal one, extinction on all patches is imminent.
We develop a novel approach to study the global behaviour of large foodwebs for ecosystems where several species share multiple resources. The model extends and generalizes some previous works and takes into account self-limitation. Under certain explicit conditions, we establish the global convergence and persistence of solutions.
We investigate stability and dynamics of large ecological networks by introducing classical methods of dynamical system theory from physics, including Hamiltonian and averaging methods. Our analysis exploits the topological structure of the network, namely the existence of strongly connected nodes (hubs) in the networks. We reveal new relations between topology, interaction structure, and network dynamics. We describe mechanisms of catastrophic phenomena leading to sharp changes of dynamics and hence completely altering the ecosystem. We also show how these phenomena depend on the structure of interaction between species. We can conclude that a Hamiltonian structure of biological interactions leads to stability and large biodiversity.
We study the biodiversity problem for resource competition systems with extinctions and self-limitationeffects. Our main result establishes estimates of biodiversity in terms of the fundamental parameters ofthe model. We also prove the global stability of solutions for systems with extinctions and large turnoverrate. We show that when the extinction threshold is distinct from zero, the large time dynamics of systemis fundamentally non-predictable. In the last part of the paper we obtain explicit analytical estimates ofecosystem robustness with respect to variations of resource supply which support the R* rule for a systemwith random parameters.
Network measures are used to predict the behavior of different systems. To be able to investigate how various structures behave and interact we need a wide range of theoretical networks to explore. Both spatial and non-spatial methods exist for generating networks but they are limited in the ability of producing wide range of network structures. We extend an earlier version of a spatial spectral network algorithm to generate a large variety of networks across almost all the theoretical spectra of the following network measures: average clustering coefficient, degree assortativity, fragmentation index, and mean degree. We compare this extended spatial spectral network-generating algorithm with a non-spatial algorithm regarding their ability to create networks with different structures and network measures. The spatial spectral network-generating algorithm can generate networks over a much broader scale than the non-spatial and other known network algorithms. To exemplify the ability to regenerate real networks, we regenerate networks with structures similar to two real Swedish swine transport networks. Results show that the spatial algorithm is an appropriate model with correlation coefficients at 0.99. This novel algorithm can even create negative assortativity and managed to achieve assortativity values that spans over almost the entire theoretical range.
Missing links due to sampling difficulties can be a limitation in network analysis. Measurements and analysis of networks with insufficient data may make the actual properties indistinct and thus include too much uncertainty to lead to accurate inferences. In addition, in dynamical networks with low link degrees and high stochasticity one sample of the network structure during a finite time window may not be sufficient for general conclusions. Our interest here is to examine the possible consequences of analysis of networks with insufficient data. We studied how mean link degree in sampled networks affects predictions of the spread of disease. Networks with weighted links were used to run scenarios that assumed distance-dependent probabilities of disease transmission when applying general simulation methodology. These scenarios were compared with scenarios including randomly drawn probabilities of disease transmission. For both types of scenarios, we also tested two link-forming methods, one based on distance-dependence and the other on a random approach. Our findings imply that sampled networks must be improved by using statistical measures before attempting to estimate or predict the spread of disease. We conclude that, under the assumption of weighted links, predictions about the extent of an epidemic can be drawn only at mean degrees that are much higher than found in empirical studies. In reality, neither sampling procedures nor disease transmissions are completely dependent on distance. Our results show how this aspect enforces an even higher level of mean degree to be present in order to achieve reasonable predictions.
Networks can be categorised using different measures of connectivity and topology of the network. We have examined if such network measures can be used as predictors of disease transmission in networks. In this study, virtual networks with a wide range of different structures are generated using the SpecNet algorithm. Measures are calculated for both the network as a whole and for individual nodes. The virtual networks generated a large variation in number of infected farms. In general, a large variation was still present for networks with equal value of a measure which implies that single network measures may not be sufficient as predictor for spread of disease. Yet, mean degree and the average clustering coefficient were the global network measures that could best explain the variation in the number of infected farms of a network. At the local level the degree and the clustering coefficient of the initially infected farm explain most of the variation in the number of infected farms. Hence, our results also points out that one should also consider the characteristics of the initially infected farm when predicting the spread of a disease.
Networks are rarely completely observed and prediction of unobserved edges is an important problem, especially in disease spread modeling where networks are used to represent the pattern of contacts. We focus on a partially observed cattle movement network in the U.S. and present a method for scaling up to a full network based on Bayesian inference, with the aim of informing epidemic disease spread models in the United States. The observed network is a 10% state stratified sample of Interstate Certificates of Veterinary Inspection that are required for interstate movement; describing approximately 20,000 movements from 47 of the contiguous states, with origins and destinations aggregated at the county level. We address how to scale up the 10% sample and predict unobserved intrastate movements based on observed movement distances. Edge prediction based on a distance kernel is not straightforward because the probability of movement does not always decline monotonically with distance due to underlying industry infrastructure. Hence, we propose a spatially explicit model where the probability of movement depends on distance, number of premises per county and historical imports of animals. Our model performs well in recapturing overall metrics of the observed network at the node level (U.S. counties), including degree centrality and betweenness; and performs better compared to randomized networks. Kernel generated movement networks also recapture observed global network metrics, including network size, transitivity, reciprocity, and assortativity better than randomized networks. In addition, predicted movements are similar to observed when aggregated at the state level (a broader geographic level relevant for policy) and are concentrated around states where key infrastructures, such as feedlots, are common. We conclude that the method generally performs well in predicting both coarse geographical patterns and network structure and is a promising method to generate full networks that incorporate the uncertainty of sampled and unobserved contacts.
Ecological and epidemiological invasions occur in a spatial context. In the study presented we tested how these processes relate to the distance dependence of spread or dispersal between spatial entities such as habitat patches or infective units. The distance dependence was described by a spatial kernel which can be characterized by its shape, quantified by kurtosis, and width, quantified by the kernel variance. We also introduced a method to analyze or generate non randomly distributed infective units or patches as point pattern landscapes. The method is based on Fourier transform and consists of two measures in the spectral representation; Continuity that relates to autocorrelation and Contrast that refers to difference in density of patches, or infective units, in different areas of the landscape. The method was also used to analyze some relevant empirical data where our results are expected to have implications for ecological or epidemiological studies. We analyzed distributions of large old trees (Quercus and Ulmus) as well as the distributions of farms (both cattle and pig) in Sweden. We tested the invasion speed in generated landscapes with different amount of Continuity and Contrast. The results showed that kurtosis, i.e. the kernel shape, was not important for predicting the invasion speed in randomly distributed patches or infective units. However, depending on the assumptions of dispersal, it may be highly important when the distribution of patches or infective units deviates from randomness, in particular when the Contrast is high. We conclude that speed of invasions and spread of diseases depends on its spatial context through the spatial kernel intertwined to the spatial structure. This implies high demands on the empirical data; it requires knowledge of both shape and width of the spatial kernel as well as spatial structure of patches or infective units.
Animals disperse in space through different movement behaviors, resulting in different displacement distances. This is often described with a displacement kernel where the long-distance dispersers are within the tail of the kernel. A displacement with a large proportion of long-distance dispersers may have impact on different aspects of spatial ecology such as invasion speed, population persistence, and distribution. It is, however, unclear whether the kurtosis of the kernel plays a major role since a fatter tail also influences the variance of the kernel. We modeled displacement in landscapes with different amounts and configurations of habitats and handled kurtosis and variance separately to study how these affected population distribution and transition time. We conclude that kurtosis is not important for any of these aspects of spatial ecology. The variance of the kernel, on the other hand, was of great importance to both population distribution and transition time. We argue that separating variance and kurtosis can cast new light on the way in which long-distance dispersers are important in ecological processes. Consequences for empirical studies are discussed.
Autocorrelation within ecological time series and synchrony between them may provide insight into the main drivers of observed dynamics. We here present methods that analyse autocorrelation and synchrony in ecological datasets using a spectral approach combined with Bayesian inference. To exemplify, we implement the method on dendrochronological data of the pedunculate oak (Quercus robur). The data consist of 110 years of growth of 10 live trees and seven trees that died during a synchronized oak death in Sweden in c. 2002-2007. We find that the highest posterior density is found for a noise colour of tree growth of gamma approximate to 0.95 (i.e. pink noise) with little difference between trees, suggesting climatic variation as a driving factor. This is further supported by the presence of synchrony, which we estimate based on phase-shift analysis. We conclude that the synchrony is time-scale dependent with higher synchrony at larger time-scales. We further show that there is no difference between the growth patterns of the alive and dead tree groups. This suggests that the trees were driven by the same factors prior to the synchronized death. We argue that this method is a promising approach for linking theoretical models with empirical data.
Between holding contacts are more common over short distances and this may have implications for the dynamics of disease spread through these contacts. A reliable estimation of how contacts depend on distance is therefore important when modeling livestock diseases. In this study, we have developed a method for analyzing distant dependent contacts and applied it to animal movement data from Sweden. The data were analyzed with two competing models. The first model assumes that contacts arise from a purely distance dependent process. The second is a mixture model and assumes that, in addition, some contacts arise independent of distance. Parameters were estimated with a Bayesian Markov Chain Monte Carlo (MCMC) approach and the model probabilities were compared. We also investigated possible between model differences in predicted contact structures, using a collection of network measures. We found that the mixture model was a much better model for the data analyzed. Also, the network measures showed that the models differed considerably in predictions of contact structures, which is expected to be important for disease spread dynamics. We conclude that a model with contacts being both dependent on, and independent of, distance was preferred for modeling the example animal movement contact data.
A method to assess the influence of between herd distances, production types and herd sizes on patterns of between herd contacts is presented. It was applied on pig movement data from a central database of Swedish Board of Agriculture. To determine the influence of these factors on the contact between holdings we used a Bayesian model and Markov chain Monte Carlo (MCMC) methods to estimate the posterior distribution of model parameters. The analysis showed that the contact pattern via animal movements is highly heterogeneous and influenced by all three factors, production type, herd size, and distance between farms. Most production types showed a positive relationship between maximum capacity and the probability of both incoming and outgoing movements. In agreement with previous studies, holdings also differed in both the number of contacts as well as with what holding types contact occurred with. Also, the scale and shape of distance dependence in contact probability was shown to differ depending on the production types of holdings.
To demonstrate how the methodology may be used for risk assessment, disease transmissions via animal movements were simulated with the model used for analysis of contacts, and parameterized by the analyzed posterior distribution. A Generalized Linear Model showed that herds with production types Sow pool center, Multiplying herd and Nucleus herd have higher risk of generating a large number of new infections. Multiplying herds are also expected to generate many long distance transmissions, while transmissions generated by Sow pool centers are confined to more local areas. We argue that the methodology presented may be a useful tool for improvement of risk assessment based on data found in central databases.
Animal movement poses a great risk for disease transmission between holdings. Heterogeneous contact patterns are known to influence the dynamics of disease transmission and should be included in modeling. Using pig movement data from Sweden as an example, we present a method for quantification of between holding contact probabilities based on different production types. The data contained seven production types: Sow pool center, Sow pool satellite, Farrow-to-finish, Nucleus herd, Piglet producer, Multiplying herd and Fattening herd. The method also estimates how much different production types will determine the contact pattern of holdings that have more than one type. The method is based on Bayesian analysis and uses data from central databases of animal movement. Holdings with different production types are estimated to vary in the frequency of contacts as well as in what type of holding they have contact with, and the direction of the contacts. Movements from Multiplying herds to Sow pool centers, Nucleus herds to other Nucleus herds, Sow pool centers to Sow pool satellites, Sow pool satellites to Sow pool centers and Nucleus herds to Multiplying herds were estimated to be most common relative to the abundance of the production types. We show with a simulation study that these contact patterns may also be expected to result in substantial differences in disease transmission via animal movements, depending on the index holding. Simulating transmission for a 1 year period showed that the median number of infected holdings was 1 (i.e. only the index holding infected) if the infection started at a Fattening herd and 2161 if the infection started on a Nucleus herd. We conclude that it is valuable to include production types in models of disease transmission and the method presented in this paper may be used for such models when appropriate data is available. We also argue that keeping records of production types is of great value since it may be helpful in risk assessments.
Studies of between herd contacts may provide important insight to disease transmission dynamics. By comparing the result from models with different level of details in the description of animal movement we studied how factors influence the final epidemic size as well as the stochastic behavior of an outbreak. We investigated the effect of contact heterogeneity of pig herds in Sweden due to herd size, between herd distance and production type. Our comparative study suggests that the production type structure is the most influential factor. Hence, our results imply that production type is the most important factor to obtain valid data for and include when modeling and analyzing this system. The study also revealed that all included factors reduce the final epidemic size and also have, yet more diverse, effects on initial rate of disease spread. This implies that a large set of factors ought to be included to assess relevant predictions when modeling disease spread between herds. Furthermore our results show that a more detailed model predicts more stochasticity in the outbreak dynamics and conclude that this is an important factor to consider in risk assessment.
Studies of between-herd contacts may provide important insight to disease transmission dynamics. By comparing the result from models with different levels of detail in the description of animal movement, we studied how factors influence the final epidemic size as well as the dynamic behaviour of an outbreak. We investigated the effect of contact heterogeneity of pig herds in Sweden due to herd size, between-herd distance and production type. Our comparative study suggests that the production-type structure is the most influential factor. Hence, our results imply that production type is the most important factor to obtain valid data for and include when modelling and analysing this system. The study also revealed that all included factors reduce the final epidemic size and also have yet more diverse effects on initial rate of disease spread. This implies that a large set of factors ought to be included to assess relevant predictions when modelling disease spread between herds. Furthermore, our results show that a more detailed model changes predictions regarding the variability in the outbreak dynamics and conclude that this is an important factor to consider in risk assessment.
Local populations in patches close to each other would probably be influenced by similar environmental conditions. When increasing the distance between the patches the local population will experience less synchronized environments. Since, the degree of synchrony is important for the overall extinction risk it is probably likewise important to include distance dependence in environmental variation when studying environmental forcing on spatially subdivided populations. Thus, we will investigate the importance of including such distance dependent synchrony when studying coloured environmental variation applied to populations in explicit landscapes. We will introduce a method based on controlling the phases when generating 1/fnoise. The results showed large differences between fast or slow density regulation responses in populations. Extinction risk was several magnitudes larger when including distance dependent synchrony compared to randomly distributing environmental time series for overcompensatory dynamics. There was one exception; it is not necessary to include distance dependent synchrony for landscape with random patch distribution. For undercompensatory dynamics the effect from distant dependent synchrony was only apparent in the most aggregated patch configurations.
We studied interplay between landscape configuration and two characteristics known to affect population extinction risks: environmental fluctuations and population dynamics. Specifically, we tested effects of noise colour (i.e. the temporal autocorrelation) and the synchrony (i.e. spatial correlation) of environmental fluctuations by simulations using models of populations with overor undercompensatory dynamics. The results demonstrated that landscape configuration has a profound effect on extinction risks. Interaction between landscape configuration and environmental fluctuations was seen as stronger effects of noise colour (decreased extinction risk with increased redness) in random landscapes and more evident effects of synchrony in aggregated landscapes. The impact of landscape structure was more striking for over- than undercompensatory dynamics; showing strongly reduced extinction risk in aggregated landscapes compared to random configurations. Results on extinction risks using data on geographical positions of old oaks (Quercus robur) concurred with those of generated landscapes. Our findings indicate that a population on the limits of its existence is extremely sensitive to both spatial configuration and temporal variation of resources. The results underline that there are no shortcuts in ecology. Correct characterization of landscape configuration, environmental fluctuations, and population dynamics is necessary when estimatin and analysing the causes of extinction risks.
Environmental variation is a major force driving fluctuations in population densities. Here, we investigated the impact of such variation on population dynamics by studying noise color and synchrony of environmental variation compared to noise color and synchrony of variation of local population densities. We used Ricker models with over- or undercompensatory density-dependent regulation, and local populations were connected by dispersal. For both noise color and synchrony, we measured the shift between environmental variation and fluctuations in population densities. We also analyzed how the shifts were affected by dispersal rate and type of density regulation. Populations with undercompensatory dynamics showed the classical picture of increasing positive shifts in synchrony with increasing dispersal rates. The color of the environmental variation also affected the positive shift in synchrony, which was increased by reddened noise in populations with undercompensatory dynamics. Populations with overcompensatory dynamics showed no shifts in synchrony regardless of dispersal rates. Shifts in noise color exhibited the same pattern: populations with overcompensatory dynamics displayed only minor shifts in noise color, whereas those with undercompensatory dynamics had distinct positive shifts that increased with dispersal rates and also depended on the degree of synchrony. These findings demonstrate that noise color and synchrony are determined by and equal to synchrony and close to the color of environmental variation in populations with overcompensatory dynamics, but differ from the corresponding aspects of environmental variation in populations with undercompensatory dynamics. The shifts in populations with undercompensatory dynamics are determined by several factors: degree of population responsiveness, dispersal rates, and both the color and synchrony of environmental variation.
The autocorrelation of environmental variation, also called noise color, influences the population dynamics and the probability of extinction risk. Increasing the distance, the variations over time for two sites will become more unsynchronized. Thus, both degree of synchrony and noise color are parts of the same environmental variation affecting population dynamics in a spatial setting. We present a novel method of generating environmental noise controlling for its noise color and synchrony. We apply these time series to carrying capacity (K) or (indirectly) to growth rate (r), and altered the population regulation response between over- and under-compensatory. A novel finding is that the qualitative effects of noise color on extinction risk do not differ with degree of synchrony. Our results for highly responsive dynamics (large growth rates and overcompensatory dynamics) agree with previous non-spatial studies by showing that the redder the noise, the lower the extinction risk. The results for less responsive dynamics are more complex, indicating that intermediate noise color causes a larger extinction risk compared to whiter or redder color. To explain this hump-shaped response, we use classical descriptions of how means and variances of population density depend on noise color. These results allow a new straightforward interpretation of how extinction risk depends on population dynamics, noise color, and synchrony.
Background: Livestock movements can affect the spread and control of contagious diseases and new data recording systems enable analysis of these movements. The results can be used for contingency planning, modelling of disease spread and design of disease control programs. Methods: Data on the Swedish cattle and pig populations during the period July 2005 until June 2006 were obtained from databases held by the Swedish Board of Agriculture. Movements of cattle and pigs were investigated from geographical and temporal perspectives, births and deaths of cattle were investigated from a temporal perspective and the geographical distribution of holdings was also investigated. Results: Most movements of cattle and pigs were to holdings within 100 km, but movements up to 1200 km occurred. Consequently, the majority of movements occurred within the same county or to adjacent counties. Approximately 54% of the cattle holdings and 45% of the pig holdings did not purchase any live animals. Seasonal variations in births and deaths of cattle were identified, with peaks in spring. Cattle movements peaked in spring and autumn. The maximum number of holdings within a 3 km radius of one holding was 45 for cattle and 23 for pigs, with large variations among counties. Missing data and reporting bias ( digit preference) were detected in the data. Conclusion: The databases are valuable tools in contact tracing. However since movements can be reported up to a week after the event and some data are missing they cannot replace other methods in the acute phase of an outbreak. We identified long distance transports of cattle and pigs, and these findings support an implementation of a total standstill in the country in the case of an outbreak of foot-and-mouth disease. The databases contain valuable information and improvements in data quality would make them even more useful.
Lettuce biomass, silver accumulation in lettuce, and effect of activity of soil microorganisms on these items, were studied in a series of experiments. Lettuce was cultivated in two kinds of soil with different organic matter concentrations. Initially the soil was either sterile or non-sterile, and had been supplied with different silver nitrate concentrations. Lettuce growth was significantly negatively affected by silver, especially in initially sterile soil with a lower organic matter content. There was also a significantly enhanced silver accumulation at larger silver supply in initially sterile soil with the lower organic matter content, otherwise there was no enhanced silver accumulation. There was a significant difference in respiration rate after harvest between the initially sterile soil and the non-sterile soil. In soil with the lower organic matter content, microorganism activity was inhibited by silver. In conclusion, silver accumulation increased and growth decreased in the lettuce grown in soil containing silver when the microorganism community in the soil had been affected by sterilization. The negative effects of silver on both lettuce and microorganisms were more distinct when the soil had a lower organic matter content.
Calculations of large-scale displacement distances were made to evaluate the combined effect of small-scale movement pattern of a Collembola, Protaphorura armata. The effect of presence of food and conspecific density on turning angle, step length and activity/motility was investigated. Calculations of net square displacement were made both by assuming correlated random walk (CRW) and by resampling data to account for correlation structures in movement patterns that violate the assumptions of CRW. In presence of food, individuals spent less time moving (decreased activity), but when they moved they showed larger turning angles than individuals moving in areas without food. Increased conspecific density did not affect time spent moving by individuals, but when step length decreased and turning angle increased. P. armata showed negative density-dependent dispersal and exhibited area-restricted search as a response to both food and increased conspecific density. The CRW was relatively robust to some violations of its underlying assumptions. However, the expected displacement increased substantially, as much as 50%, when accounting for observed auto-correlation in step length and correlation between step length and turning angle. Hence, an explanation for increased displacement and dispersal of a species can also be the result of a more complex correlation of its behaviour rather than solely altering specific movement parameters, for example increasing step length or decreasing turning angle. The results emphasise the importance of careful analysis of small-scale movement before using them as predictors of population distribution and invasion speed in heterogeneous landscapes. © 2007 Elsevier B.V. All rights reserved.
Ecology is the study of the interrelationships between organisms and their environment, including the biotic and abiotic components. There are at least six kinds of ecology: ecosystem, physiological, behavioural, population, and community. Specific topics include: acid deposition, acid rain revisited, biodiversity, biocomplexity, carbon sequestration in soils, coral reefs, ecosystem services, environmental justice, fire ecology, floods, global climate change, hypoxia, and invasion. This new book presents new research on ecology from around the world.
A spatially explicit, population-based density-independent matrix model was used to analyse the effect of landscape composition on the spatial asymptotic distribution of a population. The landscape was considered being continuous rather than consisting of patches. The redistribution of a population was viewed in a simplistic way, and modelled using a response function to local landscape quality and a displacement function. Hence, the approach is suitable for landscape ecologists. Some of the analytical methods from non-negative matrix theory were used to determine the differences between the asymptotical spatial population distribution (the dominating right eigenvector of the movement matrix) and the randomly arranged resources. The results showed that the amount and quality of poor habitats had the greatest impact on matching between population and resource distribution. The results on matching between population and resource distribution are discussed in relation to designing reserves for endangered species and in the biocontrol of pest species in agricultural systems. © 2003 Elsevier Science B.V. All rights reserved.
The effect of landscape heterogeneity on population distribution and persistence has been well investigated for habitat specialists, but the response of habitat generalist to landscape heterogeneity is less well known. We used a matrix model for an agricultural habitat generalist carabid (Pterostichus cupreus (L.)) on a lattice landscape, to study the effect of changing landscape composition and configuration on the within generation equilibrium (asymptotic) population distribution. Movements were approximated from diffusion functions that depended on habitat quality only. The population distribution of P. cupreus was sensitive to both habitat composition and configuration. Habitat configuration generally explained more variation in the population distribution relative the resources, but the changes in amount of preferred habitat had a larger effect at low amounts of preferred habitat. The resource use of P. cupreus was less sensitive when there was low contrast between habitat qualities. Numerical solutions indicated that the stable population distribution is usually reached within a generation, and the analytical results from our equilibrium model are thus reasonable approximations. The (transient) time to reach equilibrium population distribution was lower in landscapes where preferred habitat was scarce and scattered, and there was a trade-off between transient time and the population distribution relative to the resources. We found no clear threshold effects, only a gradually steeper decline in resource use as preferred resources were randomly lost in high contrast landscapes. Overall, the results were congruent with other results on generalists where demography and density dependent processes have been included, which indicate that movement alone is a driving force. © 2005 Elsevier B.V. All rights reserved.