Los Alamos National Laboratory
Earth & Environmental Sciences
Recently there have been significant advances in research on genetic strategies to control populations of diseasevectoring insects. Some of these strategies use the gene drive properties of selfish genetic elements to spread physically... more
Recently there have been significant advances in research on genetic strategies to control populations of diseasevectoring insects. Some of these strategies use the gene drive properties of selfish genetic elements to spread physically linked anti-pathogen genes into local vector populations. Because of the potential of these selfish elements to spread through populations, control approaches based on these strategies must be carefully evaluated to ensure a balance between the desirable spread of the refractoriness-conferring genetic cargo and the avoidance of potentially unwanted outcomes such as spread to non-target populations. There is also a need to develop better estimates of the economics of such releases. We present here an evaluation of two such strategies using a biologically realistic mathematical model that simulates the resident Aedes aegypti mosquito population of Iquitos, Peru. One strategy uses the selfish element Medea, a non-limited element that could permanently spread over a large geographic area; the other strategy relies on Killer-Rescue genetic constructs, and has been predicted to have limited spatial and temporal spread. We simulate various operational approaches for deploying these genetic strategies, and quantify the optimal number of released transgenic mosquitoes needed to achieve definitive spread of Medea-linked genes and/or high frequencies of Killer-Rescue-associated elements. We show that for both strategies the most efficient approach for achieving spread of anti-pathogen genes within three years is generally to release adults of both sexes in multiple releases over time. Even though females in these releases should not transmit disease, there could be public concern over such releases, making the less efficient male-only release more practical. This study provides guidelines for operational approaches to population replacement genetic strategies, as well as illustrates the use of detailed spatial models to assist in safe and efficient implementation of such novel genetic strategies.
The tree species composition of a forested landscape may respond to climate change through two primary successional mechanisms: (1) colonization of suitable habitats and (2) competitive dynamics of established species. In this study, we... more
The tree species composition of a forested landscape may respond to climate change through two primary successional mechanisms: (1) colonization of suitable habitats and (2) competitive dynamics of established species. In this study, we assessed the relative importance of competition and colonization in forest landscape response (as measured by the forest type composition change) to global climatic change. Specifically, we simulated shifts in forest composition within the Boundary Waters Canoe Area of northern Minnesota during the period 2000-2400 ad. We coupled a forest ecosystem process model, PnET-II, and a spatially dynamic forest landscape model, LANDIS-II, to simulate landscape change. The relative ability of 13 tree species to colonize suitable habitat was represented by the probability of establishment or recruitment. The relative competitive ability was represented by the aboveground net primary production. Both competitive and colonization abilities changed over time in response to climatic change. Our results showed that, given only moderate-frequent windthrow (rotation period = 500 years) and fire disturbances (rotation period = 300 years), competition is relatively more important for the shortterm (<100 years) compositional response to climatic change. For longer-term forest landscape response (>100 years), colonization became relatively more important. However, if more frequent fire disturbances were simulated, then colonization is the dominant process from the beginning of the simulations. Our results suggest that Climatic Change the disturbance regime will affect the relative strengths of successional drivers, the understanding of which is critical for future prediction of forest landscape response to global climatic change.
The identification and representation of uncertainty is recognized as an essential component in model applications. One important approach in the identification of uncertainty is sensitivity analysis. Sensitivity analysis evaluates how... more
The identification and representation of uncertainty is recognized as an essential component in model applications. One important approach in the identification of uncertainty is sensitivity analysis. Sensitivity analysis evaluates how the variations in the model output can be apportioned to variations in model parameters. One of the most popular sensitivity analysis techniques is Fourier amplitude sensitivity test (FAST). The main mechanism of FAST is to assign each parameter with a distinct integer frequency (characteristic frequency) through a periodic sampling function. Then, for a specific parameter, the variance contribution can be singled out of the model output by the characteristic frequency based on a Fourier transformation. One limitation of FAST is that it can only be applied for models with independent parameters. However, in many cases, the parameters are correlated with one another. In this study, we propose to extend FAST to models with correlated parameters. The extension is based on the reordering of the independent sample in the traditional FAST. We apply the improved FAST to linear, nonlinear, nonmonotonic and real application models. The results show that the sensitivity indices derived by FAST are in a good agreement with those from the correlation ratio sensitivity method, which is a nonparametric method for models with correlated parameters.
Two types of demographic analyses, perturbation analysis and uncertainty analysis, can be conducted to gain insights about matrix population models and guide population management. Perturbation analysis studies how the perturbation of... more
Two types of demographic analyses, perturbation analysis and uncertainty analysis, can be conducted to gain insights about matrix population models and guide population management. Perturbation analysis studies how the perturbation of demographic parameters (survival, growth, and reproduction parameters) may affect the population projection, while uncertainty analysis evaluates how much uncertainty there is in population dynamic predictions and where the uncertainty comes from. Previously, both perturbation analysis and uncertainty analysis were conducted on the long-term population growth rate. However, the population may not reach its equilibrium state, especially when there is management by harvesting or hunting. Recently, there has been an increased interest in short-term transient dynamics, which can differ from asymptotic long-term dynamics. There are currently techniques to conduct perturbation analyses of short-term transient dynamics, but no techniques have been proposed for uncertainty analysis of such dynamics. In this study, we introduced an uncertainty analysis technique, the general Fourier Amplitude Sensitivity Test (FAST), to study uncertainties in transient population dynamics. The general FAST is able to identify the amount of uncertainty in transient dynamics and contributions by different demographic parameters. We applied the general FAST to a mountain goat (Oreamnos americanus) matrix population model to give a clear illustration of how uncertainty analysis can be conducted for transient dynamics arising from matrix population models.
Many studies have been conducted to quantify the possible ecosystem/landscape response to the anticipated global warming. However, there is a large amount of uncertainty in the future climate predictions used for these studies.... more
Many studies have been conducted to quantify the possible ecosystem/landscape response to the anticipated global warming. However, there is a large amount of uncertainty in the future climate predictions used for these studies. Specifically, the climate predictions can be very different based on a variety of global climate models and alternative greenhouse emission scenarios. In this study, we coupled a forest landscape model, LANDIS-II, and a forest process model, PnET-II, to examine the uncertainty (that results from the uncertainty in the future climate predictions) in the forest-type composition prediction for a transitional forest landscape [the Boundary Water Canoe Area]. Using an improved global-sensitivity analysis technique [Fourier amplitude sensitivity test], we also quantified the amount of uncertainty in the forest-type composition prediction contributed by different climate variables including temperature, CO 2 , precipitation and photosynthetic active radiation (PAR). The forest landscape response was simulated for the period 2000-2400 AD based on the differential responses of 13 tree species under an ensemble of 27 possible climate prediction profiles (monthly time series of climate variables). Our simulations indicate that the uncertainty in the forest-type composition becomes very high after 2200 AD, which is close to the time when the current forests are largely removed by windthrow disturbances and natural mortality. The most important source of uncertainty in the forest-type composition prediction is from the uncertainty in temperature predictions. The second most important source is PAR, the third is CO 2 and the least important is precipitation. Our results also show that if the optimum photosynthetic temperature rises due to CO 2 enrichment, the forest landscape response to climatic change measured by forest-type composition may be substantially reduced.
Uncertainty and sensitivity analysis is an essential ingredient of model 1 development and applications. For many uncertainty and sensitivity analysis techniques, 2 sensitivity indices are calculated based on a relatively large sample to... more
Uncertainty and sensitivity analysis is an essential ingredient of model 1 development and applications. For many uncertainty and sensitivity analysis techniques, 2 sensitivity indices are calculated based on a relatively large sample to measure the 3 importance of parameters in their contributions to uncertainties in model outputs. To 4 statistically compare their importance, it is necessary that uncertainty and sensitivity 5 analysis techniques provide standard errors of estimated sensitivity indices. In this paper, 6 a delta method is used to analytically approximate standard errors of estimated sensitivity 7 indices for a popular sensitivity analysis method, the Fourier Amplitude Sensitivity Test 8 (FAST). Standard errors estimated based on the delta method were compared to those 9 estimated based on 20 sample replicates. We found that the delta method can provide a 10 good approximation for the standard errors of both first-order and higher-order sensitivity 11
The interplay between the carbon and other nutrient cycles is the key to understand the responses of soil ecosystems to climatic change. Using the free-air CO 2 enrichment (FACE) techniques, we carried out a multifactorial experiment in a... more
The interplay between the carbon and other nutrient cycles is the key to understand the responses of soil ecosystems to climatic change. Using the free-air CO 2 enrichment (FACE) techniques, we carried out a multifactorial experiment in a Chinese rice-wheat rotation system, to investigate the response of soil nematodes to elevated CO 2 under different application rates of N fertilizer (225.0 kg N ha À1 (HN) and 112.5 kg N ha À1 (LN), respectively) and residue incorporation (0 kg C ha À1 (ZR), 1000 kg C ha À1 (MR) and 2000 kg C ha À1 (HR), respectively). This study was conducted during the wheat growing season of 2007 after expose to the elevated CO 2 for three years. The results in our study indicated that seasonality is an important factor in determining changes in the nematode abundance and diversity. The residue addition effects were more obvious than the elevated CO 2 , which significantly influenced the abundance of total nematodes and plant-parasites, and some ecological indices. The interactions between residue addition and CO 2 significantly influenced nematode dominance and structure indices. High level of N fertilization was found to decrease the nematode diversity, generic richness and maturity indices at wheat jointing stage. There are significant interactions between N fertilization and elevated CO 2 for abundance of total nematodes and different trophic groups.
LANDIS is a cell-based spatially explicit forest landscape model designed to explore successional dynamics under natural and anthropogenic disturbances. Species age cohort (10-year cohort of a given tree species) information is required... more
LANDIS is a cell-based spatially explicit forest landscape model designed to explore successional dynamics under natural and anthropogenic disturbances. Species age cohort (10-year cohort of a given tree species) information is required for each cell in LANDIS. However, providing such information for a landscape comprising millions of cells is challenging. In this study, a stand-based assignation (SBA) approach was developed to stochastically assign species age cohorts to each cell based on forest inventory data. As a probability-based approach, SBA will introduce errors in LANDIS input. In order to assess the effect of errors produced by SBA on LANDIS results, 20 Monte Carlo simulations were produced. For each species simulated in LANDIS, the recurrence frequency (RF) of the majority species age cohort (MSAC, the most frequently occurring species age cohort) from 20 Monte Carlo simulations were used to quantify the uncertainty in species age cohorts for individual cell. Average recurrence frequency (ARF) of the MSAC was used to quantify the overall uncertainty in species age cohorts at the cell level. For each species, the coefficient of variation (CV) for the percent area and an aggregation index for the 20 Monte Carlo simulations was used to quantify the uncertainty at the landscape level. Results showed that at the cell level, uncertainty was relatively low at the beginning of the simulation (ARF was larger than 10). Seed dispersal, seedling establishment, mortality, and fire disturbance caused uncertainty to increase with simulation year. The uncertainty finally reached an equilibrium state, where input errors in original species age cohorts had little effect on the simulation outcomes. At the landscape level, species percent area and their spatial patterns were not substantially affected by the uncertainties in species age structure at the cell level. Since the typical use of LANDIS is to predict the long-term landscape pattern change, SBA can be used to parameterize species age cohorts for individual cells.
Geostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. However, due to the relatively long running time of spatially explicit forest models as a result of... more
Geostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. However, due to the relatively long running time of spatially explicit forest models as a result of their complexity, it is always infeasible to generate hundreds or thousands of Monte Carlo simulations. Thus, it is of great importance to generate a relatively small set of conditional realizations capturing most of the spatial variability. In this study, we introduced an effective sampling method (Latin hypercube sampling) into a stochastic simulation algorithm (LU decomposition simulation). Latin hypercube sampling is first compared with a common sampling procedure (simple random sampling) in LU decomposition simulation. Then it is applied to the investigation of uncertainty in the simulation results of a spatially explicit forest model, LANDIS. Results showed that Latin hypercube sampling can capture more variability in the sample space than simple random sampling especially when the number of simulations is small. Application results showed that LANDIS simulation results at the landscape level (species percent area and their spatial pattern measured by an aggregation index) is not sensitive to the uncertainty in species age cohort information at the cell level produced by geostatistical stochastic simulation algorithms. This suggests that LANDIS can be used to predict the forest landscape change at broad spatial and temporal scales even if exhaustive species age cohort information at each cell is not available.
Flooded, saturated or poorly drained soils are frequently anaerobic, leading to dissolution of the strongly magnetic minerals, magnetite and maghemite, and a corresponding decrease in soil magnetic susceptibility (MS). In this study of... more
Flooded, saturated or poorly drained soils are frequently anaerobic, leading to dissolution of the strongly magnetic minerals, magnetite and maghemite, and a corresponding decrease in soil magnetic susceptibility (MS). In this study of five temperate deciduous forests in east-central Illinois, USA, mean surface soil MS was significantly higher adjacent to upland tree species (31 Â 10 À5 SI) than adjacent to floodplain or lowland tree species (17 Â 10 À5 SI), when comparing regional soils with similar parent material of loessal silt. Although the sites differ in average soil MS for each tree species, the relative order of soil MS means for associated tree species at different locations is similar. Lowland tree species, Celtis occidentalis L., Ulmus americana L., Acer saccharinum L., Carya laciniosa (Michx. f.) Loud., and Fraxinus pennsylvanica Marsh. were associated with the lowest measured soil MS mean values overall and at each site. Tree species' flood tolerance rankings increased significantly, as soil MS values declined, the published rankings having significant correlations with soil MS values for the same species groups. The three published classifications of tree species' flood tolerance were significantly correlated with associated soil MS values at all sites, but most strongly at Allerton Park, the site with the widest range of soil drainage classes and MS values. Using soil MS measurements in forests with soil parent material containing similar initial levels of strongly magnetic minerals can provide a simple, rapid and quantitative method to classify soils according to hydric regimes, including dry conditions, and associated plant composition. Soil MS values thus have the capacity to quantify the continuum of hydric tolerances of tree species and guide tree species selection for reforestation. #
We use the LANDIS model to study the effects of planting intensity and spatial pattern of plantation on the abundance of three main species (larch (Larix gmelini), Mongolian Scotch pine (Pinus sylvestris var. Mongolica), and white birch... more
We use the LANDIS model to study the effects of planting intensity and spatial pattern of plantation on the abundance of three main species (larch (Larix gmelini), Mongolian Scotch pine (Pinus sylvestris var. Mongolica), and white birch (Betula platyphylla)) in the Tuqiang Forest Bureau on the northern slopes of Great Hing'an Mountains after a catastrophic fire in 1987. Four levels of planting intensity (covering 10%, 30%, 50%, and 70% of the severely burned area) and two spatial patterns of plantation (dispersed planting and aggregated planting) were compared in a 4 Â 2 factorial design over a 300-year period. The results showed that increasing planting intensity positively influenced larch and Mongolian Scotch pine abundance, but negatively influenced white birch abundance. However, the increased degree of larch abundance with increasing planting intensity was significantly different between intensities. The difference in larch abundance between the 10% planting intensity scenario and the 30% planting intensity scenario was greater than that between the 50% planting intensity scenario and the 70% planting intensity scenario. However, the difference between 30% and 50% planting intensity scenarios was significantly low. Hence, given considerable labor input and economic costs, 30% planting intensity would be effective for forest recovery. In addition, dispersed planting showed more promising results on forest recovery than aggregated planting. However, the difference of larch abundance between dispersed planting and aggregated planting under intermediate planting intensity scenarios (30% and 50% planting intensity) was greater than that under a low planting intensity scenario and a high planting intensity scenario. Therefore, it is necessary to incorporate spatial pattern of plantation into planting practice, especially under an intermediate planting intensity scenario. These results have important implications for forest managers to design sound forest restoration projects for landscapes affected by large infrequent disturbances. In particular, the results suggest that the current planting strategy (50% planting intensity with aggregated planting) employed after the catastrophic fire in 1987 could not be optimum, and the dispersed planting strategy covering about 30% of the severely burned area would better stimulate forest recovery. #
Anthropogenic disturbances have caused major landscape changes in the forests of northeastern China during the past 50 years. In particular, continuous over-deforestation has greatly decreased the region's forest quality. Ecological... more
Anthropogenic disturbances have caused major landscape changes in the forests of northeastern China during the past 50 years. In particular, continuous over-deforestation has greatly decreased the region's forest quality. Ecological footprint analysis generates aggregated information about a population's demand on nature and the population regional biological capacity. To show the forest change and the population's ecological demand on the study area, this paper presents an ecological footprint time series for the Songling Forestry Bureau in northeastern China from 1965 to 2000. The paper shows conventional ecological footprint time series and area demand time series -under global, Chinese and local yearly yields -to study the biological productivity of Songling. In this study, biological capacity was calculated based on a conventional approach. The results demonstrate that the ecological footprint has increased slightly and continuously during the 35-year timespan, while the biological capacity has decreased dramatically. These effects have been caused mainly by the depletion of forest resources. The results also yield much information about natural changes and socioeconomic dynamics, as well as the driving factors for these changes, of which the most important is forest management policy.
Background: Aedes aegypti is one of the most important mosquito vectors of human disease. The development of spatial models for Ae. aegypti provides a promising start toward model-guided vector control and risk assessment, but this will... more
Background: Aedes aegypti is one of the most important mosquito vectors of human disease. The development of spatial models for Ae. aegypti provides a promising start toward model-guided vector control and risk assessment, but this will only be possible if models make reliable predictions. The reliability of model predictions is affected by specific sources of uncertainty in the model.