Global realized niche divergence in the African clawed frog Xenopus laevis

Although of crucial importance for invasion biology and impact assessments of climate change, it remains widely unknown how species cope with and adapt to environmental conditions beyond their currently realized climatic niches (i.e., those climatic conditions existing populations are exposed to). The African clawed frog Xenopus laevis, native to southern Africa, has established numerous invasive populations on multiple continents making it a pertinent model organism to study environmental niche dynamics. In this study, we assess whether the realized niches of the invasive populations in Europe, South, and North America represent subsets of the species’ realized niche in its native distributional range or if niche shifts are traceable. If shifts are traceable, we ask whether the realized niches of invasive populations still contain signatures of the niche of source populations what could indicate local adaptations. Univariate comparisons among bioclimatic conditions at native and invaded ranges revealed the invasive populations to be nested within the variable range of the native population. However, at the same time, invasive populations are well differentiated in multidimensional niche space as quantified via n‐dimensional hypervolumes. The most deviant invasive population are those from Europe. Our results suggest varying degrees of realized niche shifts, which are mainly driven by temperature related variables. The crosswise projection of the hypervolumes that were trained in invaded ranges revealed the south‐western Cape region as likely area of origin for all invasive populations, which is largely congruent with DNA sequence data and suggests a gradual exploration of novel climate space in invasive populations.     

Region‐wide changes in marine ecosystem dynamics: state‐space models to distinguish trends from step changes

Regime shifts are sudden changes in ecosystem structure that can be detected across several ecosystem components. The concept that regime shifts are common in marine ecosystems has gained popularity in recent years. Many studies have searched for the step‐like changes in ecosystem state expected under a simple interpretation of this idea. However, other kinds of change, such as pervasive trends, have often been ignored. We assembled over 300 ecological time series from seven UK marine regions, covering two to three decades. We developed state‐space models for the first principal component of the time series in each region, a common measure of ecosystem state. Our models allowed both trends and step changes, possibly in combination. We found trends in three of seven regions and step changes in two of seven regions. Gradual and sudden changes are therefore important trajectories to consider in marine ecosystems.     

Population trends influence species ability to track climate change

Shifts of distributions have been attributed to species tracking their fundamental climate niches through space. However, several studies have now demonstrated that niche tracking is imperfect, that species’ climate niches may vary with population trends, and that geographic distributions may lag behind rapid climate change. These reports of imperfect niche tracking imply shifts in species’ realized climate niches. We argue that quantifying climate niche shifts and analyzing them for a suite of species reveal general patterns of niche shifts and the factors affecting species’ ability to track climate change. We analyzed changes in realized climate niche between 1984 and 2012 for 46 species of North American birds in relation to population trends in an effort to determine whether species differ in the ability to track climate change and whether differences in niche tracking are related to population trends. We found that increasingly abundant species tended to show greater levels of niche expansion (climate space occupied in 2012 but not in 1980) compared to declining species. Declining species had significantly greater niche unfilling (climate space occupied in 1980 but not in 2012) compared to increasing species due to an inability to colonize new sites beyond their range peripheries after climate had changed at sites of occurrence. Increasing species, conversely, were better able to colonize new sites and therefore showed very little niche unfilling. Our results indicate that species with increasing trends are better able to geographically track climate change compared to declining species, which exhibited lags relative to changes in climate. These findings have important implications for understanding past changes in distribution, as well as modeling dynamic species distributions in the face of climate change.     

Using Uncertainty and Sensitivity Analyses in Socioecological Agent-Based Models to Improve Their Analytical Performance and Policy Relevance

Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system.     

Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data

Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data.     

Decadal heat and drought drive body size of North American bison (Bison bison) along the Great Plains

Large grazers are visible and valuable indicators of the effects of projected changes in temperature and drought on grasslands. The grasslands of the Great Plains have supported the greatest number of bison (Bison bison; Linnaeus, 1758) since prehistoric times. We tested the hypothesis that body mass (BM, kg) and asymptotic body mass (ABM, kg) of Bison decline with rising temperature and increasing drought over both temporal and spatial scales along the Great Plains. Temporally, we modeled the relationship of annual measures of BM and height (H, m) of 5,781 Bison at Wind Cave National Park (WICA) from 1966 to 2015. We used Gompertz equations of BM against age to estimate ABM in decadal cohorts; both females and males decreased from the 1960s to the 2010s. Male ABM was variable but consistently larger (699 vs. 441 kg) than female ABM. We used local mean decadal temperature (MDT) and local mean decadal Palmer Drought Severity Index (dPDSI) to model the effects of climate on ABM. Drought decreased ABM temporally (?16 kg/local dPDSI) at WICA. Spatially, we used photogrammetry to measure body height (H_E) of 773 Bison to estimate BM_E in 19 herds from Saskatchewan to Texas, including WICA. Drought also decreased ABM spatially (?16 kg/local dPDSI) along the Great Plains. Temperature decreased ABM both temporally at WICA (?115 kg/°C local MDT) and spatially (?1 kg/°C local MDT) along the Great Plains. Our data indicate that temperature and drought drive Bison ABM presumably by affecting seasonal mass gain. Bison body size is likely to decline over the next five decades throughout the Great Plains due to projected increases in temperatures and both the frequency and intensity of drought.     

Bayesian calibration of a stochastic,multiscale agent-based model for predicting in vitro tumor growth

Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity of cell dynamics, and numerous model parameters are fundamental limitations of applying such models to predict tumor dynamics. To overcome these challenges, we have developed a coarse-grained two-scale ABM (cgABM) with a reduced parameter space that allows for an accurate and efficient calibration using a set of time-resolved microscopy measurements of cancer cells grown with different initial conditions. The multiscale model consists of a reaction-diffusion type model capturing the spatio-temporal evolution of glucose and growth factors in the tumor microenvironment (at tissue scale), coupled with a lattice-free ABM to simulate individual cell dynamics (at cellular scale). The experimental data consists of BT474 human breast carcinoma cells initialized with different glucose concentrations and tumor cell confluences. The confluence of live and dead cells was measured every three hours over four days. Given this model, we perform a time-dependent global sensitivity analysis to identify the relative importance of the model parameters. The subsequent cgABM is calibrated within a Bayesian framework to the experimental data to estimate model parameters, which are then used to predict the temporal evolution of the living and dead cell populations. To this end, a moment-based Bayesian inference is proposed to account for the stochasticity of the cgABM while quantifying uncertainties due to limited temporal observational data. The cgABM reduces the computational time of ABM simulations by 93% to 97% while staying within a 3% difference in prediction compared to ABM. Additionally, the cgABM can reliably predict the temporal evolution of breast cancer cells observed by the microscopy data with an average error and standard deviation for live and dead cells being 7.61±2.01 and 5.78±1.13, respectively.     

Modelling the predicted geographic and economic response of UK cropping systems to climate change scenarios: the case of potatoes

Geographical changes in suitability in England and Wales for the cultivation of potatoes under a climate change scenario were predicted for the years 2023 and 2065 by integrating a climate database (1951-80) with climate-driven crop growth models. Initially, model outputs were produced as point values (meteorological site locations) of predicted potential yields for current crop production. The model outputs were validated statistically using actual crop yield figures collated from bibliographic analysis. The most suitable model was run again incorporating projected temperature and precipitation changes for 2023 and 2065. These outputs were then used to predict possible economic changes to farm profitability and general market trends. Results indicated that, although yields may rise, gross margins for maincrop and especially early potatoes may also rise due to shifts in production, to a fall in overall potato output and to price increases.     

Consequences of the genetic threshold model for observing partial migration under climate change scenarios

Migration is a widespread phenomenon across the animal kingdom as a response to seasonality in environmental conditions. Partially migratory populations are populations that consist of both migratory and residential individuals. Such populations are very common, yet their stability has long been debated. The inheritance of migratory activity is currently best described by the threshold model of quantitative genetics. The inclusion of such a genetic threshold model for migratory behavior leads to a stable zone in time and space of partially migratory populations under a wide range of demographic parameter values, when assuming stable environmental conditions and unlimited genetic diversity. Migratory species are expected to be particularly sensitive to global warming, as arrival at the breeding grounds might be increasingly mistimed as a result of the uncoupling of long‐used cues and actual environmental conditions, with decreasing reproduction as a consequence. Here, we investigate the consequences for migratory behavior and the stability of partially migratory populations under five climate change scenarios and the assumption of a genetic threshold value for migratory behavior in an individual‐based model. The results show a spatially and temporally stable zone of partially migratory populations after different lengths of time in all scenarios. In the scenarios in which the species expands its range from a particular set of starting populations, the genetic diversity and location at initialization determine the species’ colonization speed across the zone of partial migration and therefore across the entire landscape. Abruptly changing environmental conditions after model initialization never caused a qualitative change in phenotype distributions, or complete extinction. This suggests that climate change‐induced shifts in species’ ranges as well as changes in survival probabilities and reproductive success can be met with flexibility in migratory behavior at the species level, which will reduce the risk of extinction.     

Shifts in the climate space of temperate cyprinid fishes due to climate change are coupled with altered body sizes and growth rates

Predictions of species responses to climate change often focus on distribution shifts, although responses can also include shifts in body sizes and population demographics. Here, shifts in the distributional ranges (‘climate space’), body sizes (as maximum theoretical body sizes, L∞) and growth rates (as rate at which L∞ is reached, K) were predicted for five fishes of the Cyprinidae family in a temperate region over eight climate change projections. Great Britain was the model area, and the model species were Rutilus rutilus, Leuciscus leuciscus, Squalius cephalus, Gobio gobio and Abramis brama. Ensemble models predicted that the species' climate spaces would shift in all modelled projections, with the most drastic changes occurring under high emissions; all range centroids shifted in a north‐westerly direction. Predicted climate space expanded for R. rutilus and A. brama, contracted for S. cephalus, and for L. leuciscus and G. gobio, expanded under low‐emission scenarios but contracted under high emissions, suggesting the presence of some climate‐distribution thresholds. For R. rutilus, A. brama, S. cephalus and G. gobio, shifts in their climate space were coupled with predicted shifts to significantly smaller maximum body sizes and/or faster growth rates, aligning strongly to aspects of temperature‐body size theory. These predicted shifts in L∞ and K had considerable consequences for size‐at‐age per species, suggesting substantial alterations in population age structures and abundances. Thus, when predicting climate change outcomes for species, outputs that couple shifts in climate space with altered body sizes and growth rates provide considerable insights into the population and community consequences, especially for species that cannot easily track their thermal niches.     

Structural features of alveolar wall basement membrane in the adult rat lung

The ultrastructural characteristics of alveolar (ABM) and capillary (CBM) basement membranes in the adult rat lung have been defined using tannic acid fixation, ruthenium red staining, or incubation in guanidine HCl. ABM is dense and amorphous, has 3- to 5-nm filaments in the lamina rara externa (facing the alveolus) that run between the lamina densa and the basal cell surface of the epithelium, has an orderly array of ruthenium red-positive anionic sites that appear predominantly (79%) on the lamina rara externa, and has discontinuities beneath alveolar type II cells but not type I cells that allow penetration of type II cytoplasmic processes into the interstitium of the alveolar wall. The CBM is fibrillar and less compact than ABM, has no lamina rara filaments, and has one fifth the number of ruthenium red- positive anionic sites of ABM that appear predominantly (64%) overlying the lamina densa. Incubation of lung tissue with Flavobacterium heparinum enzyme or with chondroitinase has shown that ABM anionic sites represent heparan sulfate proteoglycans, whereas CBM anionic sites contain this and other sulfated proteoglycans. The CBM fuses in a local fashion with ABM, compartmentalizing the alveolar wall into a thick and thin side and establishing a thin, single, basement-membrane gas-exchange surface between alveolar air, and capillary blood. The potential implications of ABM and CBM ultrastructure for permeability, cell differentiation, and repair and morphogenesis of the lung are discussed.     

A biofilm model to understand the onset of sulfate reduction in denitrifying membrane biofilm reactors

This work presents a multispecies biofilm model that describes the co‐existence of nitrate‐ and sulfate‐reducing bacteria in the H₂‐based membrane biofilm reactor (MBfR). The new model adapts the framework of a biofilm model for simultaneous nitrate and perchlorate removal by considering the unique metabolic and physiological characteristics of autotrophic sulfate‐reducing bacteria that use H₂ as their electron donor. To evaluate the model, the simulated effluent H₂, UAP (substrate‐utilization‐associated products), and BAP (biomass‐associated products) concentrations are compared to experimental results, and the simulated biomass distributions are compared to real‐time quantitative polymerase chain reaction (qPCR) data in the experiments for parameter optimization. Model outputs and experimental results match for all major trends and explain when sulfate reduction does or does not occur in parallel with denitrification. The onset of sulfate reduction occurs only when the nitrate concentration at the fiber's outer surface is low enough so that the growth rate of the denitrifying bacteria is equal to that of the sulfate‐reducing bacteria. An example shows how to use the model to design an MBfR that achieves satisfactory nitrate reduction, but suppresses sulfate reduction. Biotechnol. Bioeng. 2013; 110: 763–772. © 2012 Wiley Periodicals, Inc.     

Minimal models of multidimensional computations

The multidimensional computations performed by many biological systems are often characterized with limited information about the correlations between inputs and outputs. Given this limitation, our approach is to construct the maximum noise entropy response function of the system, leading to a closed-form and minimally biased model consistent with a given set of constraints on the input/output moments; the result is equivalent to conditional random field models from machine learning. For systems with binary outputs, such as neurons encoding sensory stimuli, the maximum noise entropy models are logistic functions whose arguments depend on the constraints. A constraint on the average output turns the binary maximum noise entropy models into minimum mutual information models, allowing for the calculation of the information content of the constraints and an information theoretic characterization of the system's computations. We use this approach to analyze the nonlinear input/output functions in macaque retina and thalamus; although these systems have been previously shown to be responsive to two input dimensions, the functional form of the response function in this reduced space had not been unambiguously identified. A second order model based on the logistic function is found to be both necessary and sufficient to accurately describe the neural responses to naturalistic stimuli, accounting for an average of 93% of the mutual information with a small number of parameters. Thus, despite the fact that the stimulus is highly non-Gaussian, the vast majority of the information in the neural responses is related to first and second order correlations. Our results suggest a principled and unbiased way to model multidimensional computations and determine the statistics of the inputs that are being encoded in the outputs.     

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.     

Alterations in alveolar basement membranes during postnatal lung growth

We studied the ultrastructural characteristics of alveolar basement membranes (ABM) and capillary basement membranes (CBM) in rat lungs at birth, at 8-10 d of age, during alveolar formation, and at 6-10 wk of age, after most alveoli have formed. We also measured in vitro lung proteoglycan and heparan sulfate synthesis at each age. We noted three major age-related changes in pulmonary basement membranes. (a) Discontinuities in the ABM through which basilar cytoplasmic foot processes extend are present beneath alveolar type-2 cells but not alveolar type-1 cells. These discontinuities are most prevalent at birth but also exist in the adult. (b) Discontinuities are also present in CBM at the two earliest time points but are maximal at 8 d of age rather than at birth. Fusions between ABM and CBM are often absent at 8 d of age, but CBM and CBM/ABM fusions were complete in the adult. (c) Heparan sulfate proteoglycans identified with ruthenium red and selective enzyme degradation are distributed equally on epithelial and interstitial sides of the ABM lamina densa at birth, but decrease on the interstitial side with age. In vitro proteoglycan and heparan sulfate accumulation at birth was two times that at 8 d and five times that in the adult. Discontinuities in ABM allow epithelial-mesenchymal interactions that may influence type-2 cells cytodifferentiation. Discontinuities in CBM suggest that capillary proliferation and neovascularization are associated with alveolar formation at 8 d. When CBM becomes complete and forms junctions with ABM, lung neovascularization likely ends as does the ability to form new alveoli.     

Gamma oscillations in primate primary visual cortex are severely attenuated by small stimulus discontinuities

Gamma oscillations (30 to 80 Hz) have been hypothesized to play an important role in feature binding, based on the observation that continuous long bars induce stronger gamma in the visual cortex than bars with a small gap. Recently, many studies have shown that natural images, which have discontinuities in several low-level features, do not induce strong gamma oscillations, questioning their role in feature binding. However, the effect of different discontinuities on gamma has not been well studied. To address this, we recorded spikes and local field potential from 2 monkeys while they were shown gratings with discontinuities in 4 attributes: space, orientation, phase, or contrast. We found that while these discontinuities only had a modest effect on spiking activity, gamma power drastically reduced in all cases, suggesting that gamma could be a resonant phenomenon. An excitatory–inhibitory population model with stimulus-tuned recurrent inputs showed such resonant properties. Therefore, gamma could be a signature of excitation–inhibition balance, which gets disrupted due to discontinuities.

Gamma oscillations (30-80 Hz) in visual cortex have been hypothesized to play an important role in feature binding, but this role has recently been questioned. This study shows that visual stimulus-induced gamma oscillations are highly attenuated with even small discontinuities in the stimulus. This "resonant" behaviour can be explained by a simple excitatory-inhibitory model in which discontinuities lead to a small reduction in lateral inputs.     

基于多源数据的城市生境网络规划方法研究与实践

生态文明建设导向下的城镇发展建设,需要切实平衡人与自然的空间发展矛盾,应用多源数据分析和判断如何建设一个体系更健康、结构更合理、功能更完善的城市绿色空间体系,具有重要的规划探索和实践意义。选定目标鸟种,研究建立其潜在生境空间参数指标体系,运用多源数据分析潜在生境、廊道和踏脚石增设需求。构建以鸟类生境为代表的特定类型生境网络、指导城市绿色空间布局。随着多源数据获取成本的逐步降低和物种类群生境参数的逐步深化发展,该方法在研究城市生境网络构建和绿色空间布局上的作用还将进一步提升。     

Climate‐related range shifts – a global multidimensional synthesis and new research directions

Poleward and upward shifts are the most frequent types of range shifts that have been reported in response to contemporary climate change. However, the number of reports documenting other types of range shifts – such as in east‐west directions across longitudes or, even more unexpectedly, towards tropical latitudes and lower elevations – is increasing rapidly. Recent studies show that these range shifts may not be so unexpected once the local climate changes are accounted for. We here provide an updated synthesis of the fast‐moving research on climate‐related range shifts. By describing the current state of the art on geographical patterns of species range shifts under contemporary climate change for plants and animals across both terrestrial and marine ecosystems, we identified a number of research shortfalls. In addition to the recognised geographic shortfall in the tropics, we found taxonomic and methodological shortfalls with knowledge gaps regarding range shifts of prokaryotes, lowland range shifts of terrestrial plants, and bathymetric range shifts of marine plants. Based on this review, we provide a research agenda for filling these gaps. We outline a comprehensive framework for assessing multidimensional changes in species distributions, which should then be contrasted with expectations based on climate change indices, such as velocity measures accounting for complex local climate changes. Finally, we propose a unified classification of geographical patterns of species range shifts, arranged in a bi‐dimensional space defined by species’ persistence and movement rates. Placing the observed and expected shifts into this bi‐dimensional space should lead to more informed assessments of extinction risks.     

Comprehensive,Population-Based Sensitivity Analysis of a Two-Mass Vocal Fold Model

Previous vocal fold modeling studies have generally focused on generating detailed data regarding a narrow subset of possible model configurations. These studies can be interpreted to be the investigation of a single subject under one or more vocal conditions. In this study, a broad population-based sensitivity analysis is employed to examine the behavior of a virtual population of subjects and to identify trends between virtual individuals as opposed to investigating a single subject or model instance. Four different sensitivity analysis techniques were used in accomplishing this task. Influential relationships between model input parameters and model outputs were identified, and an exploration of the model’s parameter space was conducted. Results indicate that the behavior of the selected two-mass model is largely dominated by complex interactions, and that few input-output pairs have a consistent effect on the model. Results from the analysis can be used to increase the efficiency of optimization routines of reduced-order models used to investigate voice abnormalities. Results also demonstrate the types of challenges and difficulties to be expected when applying sensitivity analyses to more complex vocal fold models. Such challenges are discussed and recommendations are made for future studies.     

Catastrophic Regime Shift in Water Reservoirs and S?o Paulo Water Supply Crisis

The relation between rainfall and water accumulated in reservoirs comprises nonlinear feedbacks. Here we show that they may generate alternative equilibrium regimes, one of high water-volume, the other of low water-volume. Reservoirs can be seen as socio-environmental systems at risk of regime shifts, characteristic of tipping point transitions. We analyze data from stored water, rainfall, and water inflow and outflow in the main reservoir serving the metropolitan area of São Paulo, Brazil, by means of indicators of critical regime shifts, and find a strong signal of a transition. We furthermore build a mathematical model that gives a mechanistic view of the dynamics and demonstrates that alternative stable states are an expected property of water reservoirs. We also build a stochastic version of this model that fits well to the data. These results highlight the broader aspect that reservoir management must account for their intrinsic bistability, and should benefit from dynamical systems theory. Our case study illustrates the catastrophic consequences of failing to do so.