Epidemiological risk assessment using linked network and grid based modelling: Phytophthora ramorum and Phytophthora kernoviae in the UK

We developed a stochastic simulation model incorporating most processes likely to be important in the spread of Phytophthora ramorum and similar diseases across the British landscape (covering Rhododendron ponticum in woodland and nurseries, and Vaccinium myrtillus in heathland). The simulation allows for movements of diseased plants within a realistically modelled trade network and long-distance natural dispersal. A series of simulation experiments were run with the model, representing an experiment varying the epidemic pressure and linkage between natural vegetation and horticultural trade, with or without disease spread in commercial trade, and with or without inspections-with-eradication, to give a 2 × 2 × 2 × 2 factorial started at 10 arbitrary locations spread across England. Fifty replicate simulations were made at each set of parameter values. Individual epidemics varied dramatically in size due to stochastic effects throughout the model. Across a range of epidemic pressures, the size of the epidemic was 5–13 times larger when commercial movement of plants was included. A key unknown factor in the system is the area of susceptible habitat outside the nursery system. Inspections, with a probability of detection and efficiency of infected-plant removal of 80% and made at 90-day intervals, reduced the size of epidemics by about 60% across the three sectors with a density of 1% susceptible plants in broadleaf woodland and heathland. Reducing this density to 0.1% largely isolated the trade network, so that inspections reduced the final epidemic size by over 90%, and most epidemics ended without escape into nature. Even in this case, however, major wild epidemics developed in a few percent of cases. Provided the number of new introductions remains low, the current inspection policy will control most epidemics. However, as the rate of introduction increases, it can overwhelm any reasonable inspection regime, largely due to spread prior to detection.     

Simulating the temporal pattern of waste production in farmed gilthead seabream (Sparus aurata), European seabass (Dicentrarchus labrax) and Atlantic bluefin tuna (Thunnus thynnus)

Most fish farming waste output models provide gross waste rates as a function of stocked or produced biomass for a year or total culture cycle, but without contemplating the temporality of the discharges. This work aims to ascertain the temporal pattern of waste loads by coupling available growth and waste production models and developing simulation under real production rearing conditions, considering the overlapping of batches and management of stocks for three widely cultured species in the Mediterranean Sea: gilthead seabream (Sparus aurata), European seabass (Dicentrarchus labrax) and Atlantic bluefin tuna (Thunnus thynnus). For a similar annual biomass production, the simulations showed that waste output and temporal dumping patterns differ between the three species as a result of the disparities in growth velocity, nutrient digestibility, maintenance metabolic budget and husbandry. The simulations allowed the temporal patterns including the periods of maximum discharge and the dissolved and particulate nitrogen and phosphorus content in the wastes released to be determined, both of which were seen to be species-specific.     

Modeling amphibian energetics, habitat suitability, and movements of western toads, Anaxyrus (=Bufo) boreas, across present and future landscapes

Effective conservation of amphibian populations requires the prediction of how amphibians use and move through a landscape. Amphibians are closely coupled to their physical environment. Thus an approach that uses the physiological attributes of amphibians, together with knowledge of their natural history, should be helpful. We used Niche Mapper™ to model the known movements and habitat use patterns of a population of Western toads (Anaxyrus (=Bufo) boreas) occupying forested habitats in southeastern Idaho. Niche Mapper uses first principles of environmental biophysics to combine features of topography, climate, land cover, and animal features to model microclimates and animal physiology and behavior across landscapes. Niche Mapper reproduced core body temperatures (T_c) and evaporation rates of live toads with average errors of 1.6 ± 0.4 °C and 0.8 ± 0.2 g/h, respectively. For four different habitat types, it reproduced similar mid-summer daily temperature patterns as those measured in the field and calculated evaporation rates (g/h) with an average error rate of 7.2 ± 5.5%. Sensitivity analyses indicate these errors do not significantly affect estimates of food consumption or activity. Using Niche Mapper we predicted the daily habitats used by free-ranging toads; our accuracy for female toads was greater than for male toads (74.2 ± 6.8% and 53.6 ± 15.8%, respectively), reflecting the stronger patterns of habitat selection among females. Using these changing to construct a cost surface, we also reconstructed movement paths that were consistent with field observations. The effect of climate warming on toads depends on the interaction of temperature and atmospheric moisture. If climate change occurs as predicted, results from Niche Mapper suggests that climate warming will increase the physiological cost of landscapes thereby limiting the activity for toads in different habitats.     

Computer simulation applied to the biological control of the insect Aphis gossypii for the parasitoid Lysiphlebus testaceipes

In integrated pest management (IPM), biological control is one of the possible options for the prevention or remediation of an unacceptable pest activity or damage. The success of forecast models in IPM depends, among other factors, on the knowledge of temperature effect over pests and its natural enemies. In this work, we simulated the effects of parasitism of Lysiphlebus testaceipes (Cresson, 1880) (Hymenoptera: Aphidiidae) on Aphis gossypii (Glover, 1877) (Hemiptera: Aphididae), a pest that is associated to crops of great economic importance in several parts of the world. We made use of experimental data relative to the host and its parasitoid at different temperatures. Age structure was incorporated into the dynamics through the Penna model. The results obtained showed that simulation, as a forecast model, can be a useful tool for biological control programs.     

Simulating population variation and movement within fragmented landscapes: An application to the gopher tortoise (Gopherus polyphemus)

As the human activity footprint grows, land-use decisions play an increasing role in determining the future of plant and animal species. Studies have shown that urban and agricultural development cannot only harm species populations directly through habitat destruction, but also by destroying the corridors that connect habitat patches and populations within a metapopulation. Without these pathways, populations can encounter inbreeding depression and degeneration, which can increase death rates and lower rates of reproduction. This article describes the development and application of the FRAGGLE model, a spatial system dynamics model designed to calculate connectivity indices among populations. FRAGGLE can help planners and managers identify the relative contribution of populations associated with habitat patches to future populations in those patches, taking into account the importance of interstitial land to migration success. The model is applied to the gopher tortoise (Gopherus polyphemus), a threatened species whose southeastern U.S. distribution has diminished significantly within its native range due to agricultural and urban development over the last several decades. This model is parameterized with life history and movement traits of the gopher tortoise in order to simulate population demographics and spatial distribution within an area in west-central Georgia that supports a significant tortoise population. The implications of this simulation modeling effort are demonstrated using simple landscape representations and a hypothetical on land-use management scenario. Our findings show that development resulting in even limited habitat losses (10%) may lead to significant increases in fragmentation as measured by a loss in the rate of dispersions (31%) among area subpopulations.     

A parsimonious optimal foraging model explaining mortality patterns in Serengeti wildebeest

Based on data collected over 24 years in the Serengeti in Tanzania, Sinclair and Arcese (1995) indicated that the sensitivity of blue wildebeest Connochaetes taurinus to predation risk by lions Panthera leo may cause them to change habitats between open (low risk) and wooded (risky) habitats. They found that, in poor rainfall years, predators kill wildebeest that are in better condition than those that die of natural causes. In good rainfall years, predators kill wildebeest that are in worse condition than those that die of natural causes. Sinclair and Arcese (1995) proposed the “predation-sensitive food” hypothesis. This hypothesis suggests that, as food becomes limiting, animals take greater risks to obtain more food, and some of these animals are killed. I propose a more parsimonious hypothesis based on the marginal value theorem that is consistent with the observations made by Sinclair and Arcese (1995). Wildebeest follow a single decision rule in good and poor rainfall years, viz. move when foraging elsewhere increases your rate of intake of nutritious food. Similarly, predators follow a single decision rule in good and poor rainfall years, viz. take the prey item that maximizes the intake of energy per unit effort expended. This parsimonious model does not require differences in predator sensitivity as required by Sinclair and Arcese's (1995) model. I indicate ways in which my model can be falsified.     

GIS-based modeling of the geographic distribution of Quercus emoryi Torr. (Fagaceae) in México and identification of significant environmental factors influencing the species’ distribution

The greatest concentration of oak species in the world is believed to be found in Mexico. These species are potentially useful for reforestation because of their capacity to adapt to diverse environments. Knowledge of their geographic distribution and of species–environment relations is essential for decision-making in the management and conservation of natural resources. The objectives of this study were to develop a model of the distribution of Quercus emoryi Torr. in Mexico, using geographic information systems and data layers of climatic and other variables, and to determine the variables that significantly influence the distribution of the species. The study consisted of the following steps: (A) selection of the target species from a botanical scientific collection, (B) characterization of the collecting sites using images with values or categories of the variables, (C) model building with the overlay of images that meet the habitat conditions determined from the characterization of sites, (D) model validation with independent data in order to determine the precision of the model, (E) model calibration through adjustment of the intervals of some variables, and (F) sensitivity analysis using precision and concordance non-parametric statistics applied to pairs of images. Results show that the intervals of the variables that best describe the species’ habitat are the following: altitude from 1650 to 2750 amsl, slope from 0 to 66°; average minimum temperature of January from −12 to −3 °C; mean temperature of June from 11 to 25 °C; mean annual precipitation from 218 to 1225 mm; soil units: lithosol, eutric cambisol, haplic phaeozem, chromic luvisol, rendzina, luvic xerosol, mollic planosol, pellic vertisol, eutric regosol; type of vegetation: oak forest, oak–pine forest, pine forest, pine–oak forest, juniperus forest, low open forest, natural grassland and chaparral. The resulting model of the geographic distribution of Quercus emoryi in Mexico had the following values for non-parametric statistics of precision and agreement: Kappa index of 0.613 and 0.788, overall accuracy of 0.806 and 0.894, sensitivity of 0.650 and 0.825, specificity of 0.963, positive predictive value of 0.945 and 0.957 and negative predictive value of 0.733 and 0.846. Results indicate that the variable average minimum temperature of January, with a maximum value of −3 °C, is an important factor in limiting the species’ distribution.     

Analysis of annual fluctuations of C. nodosa in the Venice lagoon: Modeling approach

A simple simulation model was developed to describe the growth trends of Cymodocea nodosa (Ucria) Ascherson based on data sets from the Venice lagoon. The model reproduces the seasonal fluctuations in the above and belowground biomass and in shoot density. The modeling results are in good agreement with data on net production, growth rates and chemical–physical parameters of water. It was assumed that light and temperature are the most important factors controlling C. nodosa development, and that the growth was not limited by nutrient availability. The aim was to simulate biomass production as a function of external forcing variables (light, water temperature) and internal control (plant density). A series of simulation experiments were performed with the basic model showing that among the most important phenomena affecting C. nodosa growth are: (1) inhibition of production and recruitment of new shoots by high temperature and (2) light attenuation due to seasonal fluctuation.     

Modeling the population dynamics of cotton bollworm Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) over a wide area in northern China

The cotton bollworm Helicoverpa armigera (Lepidoptera: Noctuidae) is one of the most serious crop pests in northern China, calling for accurate prediction of pest outbreaks and strategies for pest control. A computer model is developed to simulate the population dynamics of H. armigera over a wide area in northern China. The area considered covers 12 provinces where serious outbreaks of H. armigera have been observed. In this model, pest development is driven by local ambient temperature, and adults migrate long distances between regions and select preferred hosts for oviposition within a region. Six types of host including cotton, wheat, corn, peanut, soybean and a single category composed of all other minor hosts are considered in this model. Survival rates of eggs and larvae are based on life-table data, and simulated as a function of host type, host phenology and temperature. The incidence of diapause depends on temperature and photoperiod experienced during the larval stage. Survival rate of non-diapause pupae is a nonlinear function of rainfall, and overwinter survival rate is a nonlinear function of temperature. Insecticide is applied when population density exceeds the economic threshold on a host crop within a region. Comparisons of model output with light-trap data indicate that our model reflects the pest population dynamics over a wide area, and could potentially be used for testing novel pest control strategies in northern China.     

Incorporating behaviour into simple models of dispersal using the biological control agent Dicyphus hesperus

We explored the utility of incorporating easily measured, biologically realistic movement rules into simple models of dispersal. We depart from traditional random walk models by designing an individual-based simulation model where we decompose animal movement into three separate processes: emigration, between-patch movement, and immigration behaviour. These processes were quantified using experiments on the omnivorous insect Dicyphus hesperus moving through a tomato greenhouse. We compare the predictions of the individual-based model, along with a series of biased random walk models, against an independent experimental release of D. hesperus. We find that in this system, the short-term dispersal of these insects is described well by our individual-based model, but can also be described by a 2D grid-based biased random walk model when mortality is accounted for.     

Application of a temperature-dependent von Bertalanffy growth model to bullhead (Cottus gobio)

The most studied and commonly applied model of fish growth is the von Bertalanffy model. However, this model does not take water temperature into account, which is one of the most important environmental factors affecting the life cycle of fish, as many physiological processes that determine growth, e.g. metabolic rate and oxygen supply, are directly influenced by temperature. In the present study we propose a version of the von Bertalanffy growth model that includes mean annual water temperatures by correlating the growth coefficient, k, explicitly and the asymptotic length, L_∞, implicitly to water temperature. All relationships include parameters with an obvious biological relevance that makes them easier to identify. The model is used to fit growth data of bullhead (Cottus gobio) at different locations in the Bez River network (Drme, France). We show that temperature explains much of the growth variability at the different sampling sites of the network.     

Determining the community structure of the coral Seriatopora hystrix from hydrodynamic and genetic networks

The exchange of genetic information between coral reefs through the transport of larvae can be described in terms of networks that capture the linkages between distant populations. A key question arising from these networks is the determination of the highly connected modules (communities). Communities can be defined using genetic similarity or distance statistics between multiple samples but due to limited specimen sampling capacity the boundaries of the communities for the known coral reefs in the seascape remain unresolved. In this study we use the microsatellite composition of individual corals to compare sample populations using a genetic dissimilarity measure (F_ST) which is then used to create a complex network. This network involved sampling 1025 colonies from 22 collection sites and examining 10 microsatellites loci. The links between each sampling site were given a strength that was created from the pair wise F_ST values. The result is an undirected weighted network describing the genetic dissimilarity between each sampled population. From this network we then determined the community structure using a leading eigenvector algorithm within graph theory. However, given the relatively limited sampling conducted, the representation of the regional genetic structure was incomplete. To assist with defining the boundaries of the genetically based communities we also integrated the communities derived from a hydrodynamic and distance based networks. The hydrodynamic network, though more comprehensive, was of smaller spatial extent than our genetic sampling. A Bayesian Belief network was developed to integrate the overlapping communities. The results indicate the genetic population structure of the Great Barrier Reef and provide guidance on where future genetic sampling should take place to complete the genetic diversity mapping.     

On the sudden disappearance of Egeria densa from a Ramsar wetland site of Southern Chile: A climatic event trigger model

Contemporary shallow lakes theory proposes that these ecosystems may experience abrupt regime shifts due to small changes in controlling variables or triggers. So far, these triggers have been related mostly to nutrients as the immediate driver. During May 2004 the río Cruces wetland, a Ramsar site located in Southern Chile, underwent a major regime shift, from a clear water state, vastly dominated by the invasive macrophyte Egeria densa, to a turbid water state. In this article we show, through the analysis of long-term meteorological data that late fall 2004 was anomalous due to the presence of a high-pressure cell that persisted most of the month of May over Southern Chile. This climatic event caused an almost complete absence of precipitations and lower temperatures during this period, including several freezing nights. Eco-physiological experiments showed that 6 h exposure to desiccation kill the macrophyte. We developed a simple-biology dynamic model, under Stella Research 9.1, to show that the climatic anomaly of May 2004, plus the increased sedimentation of the wetland's floodplains, and the associated response of E. densa, explains its sudden disappearance from río Cruces wetland.     

A discrete model for estimating the development time from egg to infecting larva of Ostertagia ostertagi parametrized using a fuzzy rule-based system

Ostertagia ostertagi is a nematode, predominantly affecting cattle in the Pampean region of Argentina. A mathematical model parametrized using fuzzy rule-based systems of the Takagi-Sugeno-Kant type (FTSK) for estimating the development time from egg to infecting larval stage L3 of the gastrointestinal parasite O. ostertagi is here proposed. The estimation of development time of O. ostertagi is essential for the generation of appropriate control mechanisms, since this provides information about the time when parasites are ready to migrate to pastures. For the purpose of reflecting the natural environmental conditions, the mean daily temperature is taken as the main and only regulator of the development time. Humidity conditions are considered to be sufficient for the normal development of the larvae. Hence the individual's daily growth is a function of its length and the mean temperature recorded on the previous day. It is expressed in terms of a difference equation with fuzzy parameters, which are defined using laboratory data. Model outputs are tested against results of field experiments. Simulation results are very satisfactory, yielding a mean estimation error (MEE) of 0.64 weeks, with variance 0.34, and a determination coefficient R² = 0.74. The model clearly exhibits an inverse relationship between development time and temperature both in controlled and in field conditions. It also exhibits a very sensitive response both to the order in which the temperature sequence occurs, - reproducing the differences observed between spring and autumn - and to the amplitude of the temperature range.     

Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy

We present a modelling framework that combines machine learning techniques and Geographic Information Systems to support the management of an important aquaculture species, Manila clam (Ruditapes philippinarum). We use the Venice lagoon (Italy), the first site in Europe for the production of R. philippinarum, to illustrate the potential of this modelling approach. To investigate the relationship between the yield of R. philippinarum and a set of environmental factors, we used a Random Forest (RF) algorithm. The RF model was tuned with a large data set (n = 1698) and validated by an independent data set (n = 841). Overall, the model provided good predictions of site-specific yields and the analysis of marginal effect of predictors showed substantial agreement among the modelled responses and available ecological knowledge for R. philippinarum. The most influent environmental factors for yield estimation were percentage of sand in the sediment, salinity, and water depth. Our results agree with findings from other North Adriatic lagoons. The application of the fitted RF model to continuous maps of all the environmental variables allowed estimates of the potential yield for the whole basin. Such a spatial representation enabled site-specific estimates of yield in different farming areas within the lagoon. We present a possible management application of our model by estimating the potential yield under the current farming distribution and comparing it to a proposed re-organization of the farming areas. Our analysis suggests a reduction of total yield is likely to result from the proposed re-organization.     

Identifying key life-traits for the dynamics and gene flow in a weedy crop relative: Sensitivity analysis of the GeneSys simulation model for weed beet (Beta vulgaris ssp. vulgaris)

The benefits of genetically modified herbicide-tolerant (GMHT) sugar beet (Beta vulgaris) varieties stem from their presumed ability to improve weed control and reduce its cost, particularly targeting weed beet, a harmful annual weedy form of the genus Beta (i.e. B. vulgaris ssp. vulgaris) frequent in sugar beet fields. As weed beet is totally interfertile with sugar beet, it is thus likely to inherit the herbicide-tolerance transgene through pollen-mediated gene flow. Hence, the foreseeable advent of HT weed beet populations is a serious threat to the sustainability of GM sugar beet cropping systems. For studying and quantifying the long-term effects of cropping system components (crop succession and cultivation techniques) on weed beet population dynamics and gene flow, we developed a biophysical process-based model called GeneSys-Beet in a previous study. In the present paper, the model was employed to identify and rank the weed life-traits as function of their effect on weed beet densities and genotypes, using a global sensitivity analysis to model parameters. Monte Carlo simulations with simultaneous randomization of all life-trait parameters were carried out in three cropping systems contrasting for their risk for infestation by HT weed beets. Simulated weed plants and bolters (i.e. beet plants with flowering and seed-producing stems) were then analysed with regression models as a function of model parameters to rank processes and life-traits and quantify their effects. Key parameters were those determining the timing and success of growth, development, seed maturation and the physiological end of seed production. Timing parameters were usually more important than success parameters, showing for instance that optimal timing of weed management operations is more important than its exact efficacy. The ranking of life-traits though depended on the cropping system and, to a lesser extent, on the target variable (i.e. GM weeds vs. total weed population). For instance, post-emergence parameters were crucial in rotations with frequent sugar beet crops whereas pre-emergence parameters were most important when sugar beet was rare. In the rotations with frequent sugar beet and insufficient weed control, interactions between traits were small, indicating diverse populations with contrasted traits could prosper. Conversely, when sugar beet was rare and weed control optimal, traits had little impact individually, indicating that a small number of optimal combinations of traits would be successful. Based on the analysis of sugar beet parameters and genetic traits, advice for the future selection of sugar beet varieties was also given. In climatic conditions similar to those used here, the priority should be given to limiting the presence of hybrid seeds in seed lots rather than decreasing varietal sensitivity to vernalization.     

COSMOS,a spatially explicit model to simulate the epidemiology of Cosmopolites sordidus in banana fields

A stochastic individual-based model called COSMOS was developed to simulate the epidemiology of banana weevil Cosmopolites sordidus, a major pest of banana fields. The model is based on simple rules of local movement of adults, egg laying of females, development and mortality, and infestation of larvae inside the banana plants. The biological parameters were estimated from the literature, and the model was validated at the small-plot scale. Simulated and observed distributions of attacks were similar except for five plots out of 18, using a Kolmogorov–Smirnov test. These exceptions may be explained by variation in predation of eggs and measurement error. An exhaustive sensitivity analysis using the Morris method showed that predation rate of eggs, demographic parameters of adults and mortality rate of larvae were the most influential parameters. COSMOS was therefore used to test different spatial arrangements of banana plants on the epidemiology of C. sordidus. Planting bananas in groups increased the time required to colonise plots but also the percentage of banana plants with severe attacks. Spatial heterogeneity of banana stages had no effect on time required to colonise plots but increased the mean level of attacks. Our model helps explain key factors of population dynamics and the epidemiology of this tropical pest.     

Integrating farming techniques in an ecological matrix model: Implementation on the primrose (Primula vulgaris)

Several studies have proven the importance of field margins in sustaining biodiversity and other work has been done on the effect of field management on field margin flora. However few models have been built to predict the effects of field management on the flora. Our project addresses this need for a model capable of predicting the effect of cropping techniques and their timing on the flora of field margins. Primula vulgaris is a biodiversity indicator, characteristic of undisturbed flora and found in field margins and woodlands: its population has been declining for several years. We created a temporal matrix model of P. vulgaris populations on field margins, taking into account the effects of field, field margin and roadside management based on literature and expert knowledge. We then analysed its sensitivity to demographic parameters by comparing lambda (growth rate) sensitivity and elasticity. We compared the management parameter effect using the relative growth rate of the population after 6 years of simulation. Sensitivity analysis to biological parameters showed the importance of adult survival and seed production and germination. Results show that P. vulgaris is particularly sensitive to broad-spectrum herbicides and that other management techniques like early mowing, scything and scrub-killer (diluted broad-spectrum herbicide or specific herbicide) are less aggressive. Our simulations show that management of cash crops in Brittany is too aggressive for P. vulgaris populations and that 4-5 years of grassland in the adjacent field are necessary to maintain populations.     

Influence of host migration between woodland and pasture on the population dynamics of the tick Ixodes ricinus: A modelling approach

Ticks act as vectors of pathogens that can be harmful to animals and/or humans. Epidemiological models can be useful tools to investigate the potential effects of control strategies on diseases such as tick-borne diseases. The modelling of tick population dynamics is a prerequisite to simulating tick-borne diseases and the corresponding spread of the pathogen. We have developed a dynamic model to simulate changes in tick density at different stages (egg, larva, nymph and adult) under the influence of temperature. We have focused on the tick Ixodes ricinus, which is widespread in Europe. The main processes governing the biological cycles of ticks were taken into account: egg laying, hatching, development, host (small, mainly rodents, or large, like deer and cattle, mammals) questing, feeding and mortality. This model was first applied to a homogeneous habitat, where simulations showed the ability of the model to reproduce the general patterns of tick population dynamics. We considered thereafter a multi-habitat model, where three different habitats (woodland, ecotone and meadow) were connected through host migration. Based on this second application, it appears that migration from woodland, via the ecotone, is necessary to sustain the presence of ticks in the meadow. Woodland can therefore be considered as a source of ticks for the meadow, which in turn can be regarded as a sink. The influence of woodland on surrounding tick densities increases in line with the area of this habitat before reaching a plateau. A sensitivity analysis to parameter values was carried out and demonstrated that demographic parameters (sex ratio, development, mortality during feeding and questing, host finding) played a crucial role in the determination of questing nymph densities. This type of modelling approach provides insight into the influence of spatial heterogeneity on tick population dynamics.     

Waterfowls habitat modeling: Simulation of nest site selection for the migratory Little Tern (Sterna albifrons) in the Nakdong estuary

This paper aims to find patterns in nest site selection by Little Terns Sterna albifrons, in the Nakdong estuary in South Korea. This estuary is important waterfowl stopover and breeding habitat, located in the middle of the East Asia-Australasian Flyway. The Little Tern is a common species easily observed near the seashore but their number is gradually declining around the world. We investigated their nests and eggs on a barrier islet in the Nakdong estuary during the breeding season (May to June, 2007), and a pattern for the nest site selection was identified using genetic programming (GP). The GP generated a predictive rule-set model for the number of Little Tern nests (training: R² = 0.48 and test: 0.46). The physical features of average elevation, variation of elevation, plant coverage, and average plant height were estimated to determine the influence on nest numbers for Little Tern. A series of sensitivity analyses stressed that mean elevation and vegetation played an important role in nest distribution for Little Tern. The influence of these two variables could be maximized when elevation changed moderately within the sampled quadrats. The study results are regarded as a good example of applying GP to vertebrate distribution patterning and prediction with several important advantages compared to conventional modeling techniques, and can help establish a management or restoration strategy for the species.