A spatiotemporal individual-based fish model to investigate emergent properties at the organismal and the population level

A spatiotemporal individual-based model (IBM) of roach (Rutilus rutilus) including bioenergetic principles was used to study emergent properties at the individual and the population level which appeared as a result of resource allocation and time scheduling of activities related to maintenance, growth, and reproduction in a seasonally changing environment. The model was used as a virtual laboratory to consider the impact of various parameters on vital rates and spatial behaviour of roach, which is particularly difficult to study in the field. The parameterisation based on field studies performed at Lake Belau (Schleswig-Holstein, Germany) and laboratory measurements. This allowed us to explore the relationship between population structure and environmental heterogeneity on an integrated base. At the individual level emergent properties such as spatial behaviour, growth, and food consumption could be identified. Emergent properties at the population level resulted as phenotypic consequences of a trophic bottleneck, the influence of lake morphology on the phenotype as well as size-dependent winter mortality rates and post-reproductive mortality rates.     

Pattern formation and individual-based models: The importance of understanding individual-based movement

This paper demonstrates that while pattern formation can stabilize individual-based models of predator–prey systems, the same individual-based models also allow for stabilization by alternate mechanisms, particularly localized consumption or diffusion limitation. The movement rules of the simulation are the critical feature which determines which of these mechanisms stabilizes any particular predator–prey individual-based model. In particular, systems from well-connected subpopulations, in each of which a predator can attack any prey, generally exhibit stabilization by pattern formation. In contrast, when restricted movement within a (sub-)population limits the ability of predators to consume prey, localized consumption or diffusion limitation can stabilize the system. Thus while the conclusions from differential equations on the role of pattern formation for stability may apply to discrete and noisy systems, it will take a detailed understanding of movement and scales of interaction to examine the role of pattern formation in real systems. Additionally, it will be important to link an understanding of both foraging and inter-patch movement, since by analogy to the models, both would be critical for understanding how real systems are stabilized by being discrete and spatial.     

Microbial productivity in variable resource environments

The rate, timing, and quality of resource supply exert strong controls on a wide range of ecological processes. In particular, resource-mediated changes in microbial activity have the potential to alter ecosystem processes, including the production and respiration of organic matter. In this study, we used field experiments and simulation modeling to explore how aquatic heterotrophic bacteria respond to variation in resource quality (low vs. high) and resource schedule (pulse vs. press). Field experiments revealed that one-time pulse additions of resources in the form of dissolved organic carbon (DOC) caused short-lived (< or =48 h) peaks in bacterial productivity (BP), which translated into large differences across treatments: cumulative BP was twice as high in the pulse vs. press treatment under low resource quality, and five times as high under high resource quality. To gain a more mechanistic understanding of microbial productivity in variable resource environments, we constructed a mathematical model to explore the attributes of bacterial physiology and DOC supply that might explain the patterns observed in our field experiments. Model results suggest that the mobilization rate of refractory to labile carbon, an index of resource quality, was critical in determining cumulative differences in BP between pulse and press resource environments (BPPu:Pr ratios). Moreover, BPPu:Pr ratios were substantially larger when our model allowed for realistic changes in bacterial growth efficiency as a function of bacterial carbon consumption. Together, our field and modeling results imply that resource schedule is important in determining the flow of material and energy from microbes to higher trophic levels in aquatic food webs, and that the effects of resource quality are conditional upon resource schedule. An improved understanding of the effects of resource variability on microorganisms is therefore critical for predicting potential changes in ecosystem functioning in response to environmental change, such as altered DOC fluxes from terrestrial to aquatic ecosystems.     

Development and validation of an individual based Daphnia magna population model: The influence of crowding on population dynamics

An individual-based model was developed to predict the population dynamics of Daphnia magna at laboratory conditions from individual life-history traits observed in experiments with different feeding conditions. Within the model, each daphnid passes its individual life cycle including feeding on algae, aging, growing, developing and – when maturity is reached – reproducing. The modelled life cycle is driven by the amount of ingested algae and the density of the Daphnia population. At low algae densities the population dynamics is mainly driven by food supply, when the densities of algae are high, the limiting factor is “crowding” (a density-dependent mechanism due to chemical substances released by the organisms or physical contact, but independent of food competition).     

Optimal annual routines: new tools for conservation biology?

Many applied problems in ecology and conservation require prediction, and population models are important tools for that purpose. Formerly, the majority of predictive population models were based on matrix models. As the limitations of classical matrix models have become clearer, the use of individual-based models has increased. These models use behavioral rules imposed at the level of the individual to establish the emergent consequences of those rules at the population level. Individual behaviors in such models use an array of different rule types, from empirically derived probabilities to long-term fitness considerations. There has been surprisingly little discussion of the strengths and weaknesses of these different rule types. Here, we consider different strategies for modeling individual behaviors, together with some problems associated with individual-based models. We propose a novel approach based on modeling optimal annual routines. Annual routines allow individual behaviors to be predicted over a whole annual cycle within the context of long-term fitness considerations. Temporal trade-offs between different behaviors are automatically included in annual routine models, overcoming some of the primary limitations of other individual-based models. Furthermore, as well as population predictions, individual behaviors and indices of condition are emergent features of annual routine models. We show that these can be more sensitive to environmental change than population size, offering alternative, repeatable metrics for monitoring population status. Annual routine models provide no panacea for the problems of data limitations in predictive population modeling. However, as a result of their ability to deal with life-history trade-offs, as well as their potential for relatively rapid and accurate validation and parameterization, we suggest that annual routine models have strong potential for predictive population modeling in applied conservation settings.     

Dissecting the drivers of population cycles: Interactions between parasites and mountain hare demography

There is a growing awareness that cyclic population dynamics in vertebrate species are driven by a complex set of interactions rather than a single causal factor. While theory suggests that direct host-parasite interactions may destabilise population dynamics, the interaction between host and parasite may also influence population dynamics through indirect effects that result in delayed responses to either density or to life-history traits. Using empirical data on mountain hares (Lepus timidus) infected with a nematode parasite (Trichostrongylus retortaeformis), we developed an individual-based model (IBM) that incorporated direct effects and delayed life-history effects (DLHEs) of a macroparasite, alternative transmission mechanisms and seasonality in host population dynamics. The full model describes mean characteristics of observed mountain hare time series and parasite abundance, but by systematically removing model structure we dissect out dynamic influences of DLHEs. The DLHEs were weakly destabilising, increasing the propensity for cyclic dynamics and suggesting DLHEs could be important processes in host-parasite systems. Further, by modifying model structure we identify a strong influence of parasite transmission mechanism on host population stability, and discuss the implications for parasite aggregation mechanisms, host movement and natural geographical variation in host population dynamics. The effect of T. retortaeformis on mountain hares likely forms part of a complex set of interactions that lead to population cycles.     

Variable effects of a generalist parasitoid on a biocontrol seed predator and its target weed

Biological control (the importation of enemies from an invader's native range) is often considered our best chance of controlling the most widespread invaders. Ideally, the agent reduces invader abundance to some acceptably low level, and the two coexist at low density with the agent providing continuous control over the long-term. But the outcome may be complicated when the agent is attacked by native predators and parasites. We used a spatially explicit, discrete-time, individual-based, coupled plant-seed predator-parasitoid model to estimate the impact of the biocontrol agent Eustenopus villosus (a seed predator) on the invasive, annual weed Centaurea solstitialis, both with and without the generalist parasitoid Pyemotes tritici. We estimated the agent's ability to reduce plant density, spread rate, and population growth rate over 50 years. We used long-term demographic data from two sites in central California, USA, to parameterize the model and assess how populations in different climatic zones might respond differently to the agent and the parasitoid. We found that the biocontrol agent reduced plant density (relative to predictions for an uncontrolled invasion), but its impact on the invader's spread rate was modest and inconsistent. The agent had no long-term impact on population growth rate (lambda). Parasitism caused a trophic cascade, the strength of which varied between sites. At our coastal site, the parasitoid entirely eliminated the impact of the agent on the plant. At our Central Valley site, even when parasitized, the agent significantly reduced plant density and spread rate over several decades (although to a lesser degree than when it was not parasitized), but not invader lambda. Surprisingly, we also found that the length of time the invader was allowed to spread across the landscape prior to introducing the agent (5, 25, or 50 years) had little influence over its ability to control the weed in the long-term. This is encouraging news for land managers attempting to control invasive plants that have already established widespread, high-density populations. Unfortunately, our results also show that attack by the native generalist parasitoid had a larger influence over how effectively the agent reduced invader performance.     

Development of an individual-based model to evaluate elk (Cervus elaphus nelsoni) movement and distribution patterns following the Cerro Grande Fire in north central New Mexico, USA

Though studies have modeled the effects of fires on elk, no studies have related the effects of post-fire landscape succession on ungulate movements and distribution using dynamic modeling techniques. The purpose of this study was to develop and test a spatially-explicit, stochastic, individual-based model (IBM) to evaluate potential movement and distribution patterns of elk (Cervus elaphus nelsoni) in relation to spatial and temporal aspects of the Cerro Grande Fire that burned north central New Mexico in May of 2000. Following extensive literature review, the SAVANNA Ecosystem Model was selected to simulate the underlying post-fire successional processes driving elk movement and distribution. Standard logisitic regression was used to analyze habitat-use patterns of ten elk from data collected using global positioning system radio collars while an additional five animals were used as an independent test set during model validation. Static variables in the form of roads, buildings, fences, and habitual use/memory were used to modify a map of impedance values based on the logistic regression of slope, aspect, and elevation. Integration with SAVANNA came through the application of a habitat suitability index (HSI), which combined movement rules written for the IBM and variables modified and produced by the dynamic ecological processes run in SAVANNA. Overall pattern analysis indicated that realistic migrational processes and habitat-use patterns emerged from movement rules incorporated into the IBM in response to advancing and receding snow when compared to the independent test set. Primary and secondary movement pathways emerged from the collective responses of simulated individuals. Using regression analyses, no significant differences between simulated animals and animals used in either model development or an independent test set revealed any differences in response to snow patterns. These considerations suggest the model was adequately corroborated based on existing data and outlined objectives.     

Chemically mediated competition between microbes and animals: microbes as consumers in food webs

Microbes are known to affect ecosystems and communities as decomposers, pathogens, and mutualists. However, they also may function as classic consumers and competitors with animals if they chemically deter larger consumers from using rich food-falls such as carrion, fruits, and seeds that can represent critical windfalls to both microbes and animals. Microbes often use chemicals (i.e., antibiotics) to compete against other microbes. Thus using chemicals against larger competitors might be expected and could redirect significant energy subsidies from upper trophic levels to the detrital pathway. When we baited traps in a coastal marine ecosystem with fresh vs. microbe-laden fish carrion, fresh carrion attracted 2.6 times as many animals per trap as microbe-laden carrion. This resulted from fresh carrion being found more frequently and from attracting more animals when found. Microbe-laden carrion was four times more likely to be uncolonized by large consumers than was fresh carrion. In the lab, the most common animal found in our traps (the stone crab Menippe mercenaria) ate fresh carrion 2.4 times more frequently than microbe-laden carrion. Bacteria-removal experiments and feeding bioassays using organic extracts of microbe-laden carrion showed that bacteria produced noxious chemicals that deterred animal consumers. Thus bacteria compete with large animal scavengers by rendering carcasses chemically repugnant. Because food-fall resources such as carrion are major food subsidies in many ecosystems, chemically mediated competition between microbes and animals could be an important, common, but underappreciated interaction within many communities.     

A new synergetic paradigm in environmental numerical modeling: Hybrid models combining deterministic and machine learning components

A new type of environmental numerical models, hybrid environmental numerical models (HEMs) based on combining deterministic modeling and machine learning components, is introduced and formulated. Conceptual and practical possibilities of developing HEM, as an optimal synergetic combination of the traditional deterministic/first principles modeling (like that used for solving PDEs on the sphere representing model dynamics of global climate models) and machine learning components (like accurate and fast neural network emulations of model physics or chemistry processes), are discussed. Examples of developed HEMs (hybrid climate models and a hybrid wind–wave ocean model) illustrate the feasibility and efficiency of the new approach for modeling extremely complex multidimensional systems.     

A bio-inspired computing model as a new tool for modeling ecosystems: The avian scavengers as a case study

The models used for ecosystems modeling are generally based on differential equations. However, in recent years new computational models based on biological processes, or bioinspired models, have arisen, among which are P systems. These are inspired by the functions of cells and present important advantages with respect to traditional models, such as a high computational efficiency, modularity and their ability to work in parallel. They are simple, individual-based models that use biological parameters that can be obtained experimentally. In this work, we present the framework for a model based on P systems applied to the study of an ecosystem in which three avian scavengers (predators) interact with 10 wild and domestic ungulates (preys). The computation time for 100 repetitions, corresponding to 14 simulation years each, with an initial population composed of 385,422 individuals, was 30 min. Our results suggest that the model presented, based on P systems, correctly simulates the population dynamics in the period of time analyzed. We discuss the usefulness of this tool in simulating complex ecosystems dynamics to aid managers, conservationists and policy-makers in making appropriate decisions for the improvement of management and conservation programs.     

Modeling individual and population dynamics in a consumer–resource system: Behavior under food limitation and crowding and the effect on population cycling in Daphnia

In population modeling, a considerable level of complexity is often required to provide trustworthy results, comparable with field observations. By assuring sufficient detail at the individual level while preserving the potential to explore the consequences at higher levels, individual-based modeling may thus provide a useful tool to investigate dynamics at different levels of organization. Still, population dynamics resulting from such models are often at odds with observations from the field. This may be partly caused by a lack of focus on the individual dynamics under conditions of food stress and starvation. I developed a physiologically structured, individual-based simulation model to investigate life history of Daphnia and its effect on population dynamics in response to the productivity of the system. In verifying model behavior with available literature data on life history and physiology, I paid special attention to the dynamics of food intake and the verification of individual level results under conditions of food limitation and starvation. I show that the maximum filtering rates under low food levels used in the current model are much closer to measured filtering rates than the ones used in other models. Being consistent with results on physiology and life history from experiments at a wide range of food availability (including starvation), the model generates low amplitude or high amplitude population density cycles depending on the productivity of the system, as observed in field and experimental populations of Daphnia and with the minimum population densities being one to two orders of magnitude lower in the high amplitude than in the low amplitude cycles. To generate results which are not only qualitatively but also quantitatively comparable to experimental and field observations, however, a crowding effect on the filtering response has to be incorporated in the model.     

Modeling fecundity in birds: Conceptual overview, current models, and considerations for future developments

Fecundity is fundamental to the fitness, population dynamics, conservation, and management of birds. For all the efforts made to measure fecundity or its surrogates over the past century of avian research, it is still mismeasured, misrepresented, and misunderstood. Fundamentally, these problems arise because of partial observability of underlying processes such as renesting, multiple brooding, and temporary emigration. Over the last several decades, various analytical approaches have been developed to estimate fecundity from incomplete and biased data. These, include scalar arithmetic formulae, partial differential equations, individual-based simulations, and Markov chain methodology. In this paper, we: (1) identify component processes of avian reproduction; (2) review existing methods for modeling fecundity; (3) place these diverse models under a common conceptual framework; (4) describe the parameterization, validation, and limitations of such models; and (5) point out future considerations and challenges in the application of fecundity models. We hope this synthesis of existing literature will help direct researchers toward the most appropriate methods to assess avian reproductive success for answering questions in evolutionary ecology, natural history, population dynamics, reproductive toxicology, and management.     

镉在北京褐潮土中对玉米幼苗及其根际微生物的毒性效应

通过温室盆栽实验,研究了镉在北京褐潮土中对玉米(品种郑单958)幼苗的毒性效应及其生物富集特性,并通过聚合酶链式反应-变性梯度凝胶电泳(PCR-DGGE)技术,探讨了镉对玉米根际微生物群落结构的影响。结果表明,高浓度(>100mg·kg-1)镉对玉米幼苗的生长发育有明显的抑制作用,北京褐潮土中镉引起玉米幼苗株高下降1/2的效应浓度(EC50)为654.6mg·kg-1,引起玉米根部和地上部干质量下降1/2的EC50分别为323.6和110.2mg·kg-1,玉米幼苗地上部生物量(干质量)可作为评价重金属生态毒性的适宜终点。玉米幼苗对镉有一定的吸收累积效应,镉在玉米幼苗各组织中的浓度分布为根>茎>叶,其中根部对镉有一定的富集作用(生物富集系数BAF>1)。镉污染可引起玉米根际微生物群落结构发生改变,高浓度(1000mg·kg-1)镉可导致部分微生物种群数量减少甚至完全消失,表明镉污染可对植物幼苗、植物根际微生物以及植物-微生物之间的相互作用造成重要的干扰和威胁。     

Projecting population trends of endangered amphibian species in the face of uncertainty: A pattern-oriented approach

Amphibian populations have been declining worldwide for the last three decades. Determining the risk of extinction is one of the major goals of amphibian conservation, yet few quantitative models have been developed for amphibian populations. Like most rare or threatened populations, there is a paucity of life history data available for most amphibian populations. Data on the critical juvenile life stage are particularly lacking. Pattern oriented modeling (POM) has been used successfully to estimate life history parameters indirectly when critical data lacking, but has not been applied to amphibian populations. We describe a spatially explicit, individual-based, stochastic simulation model developed to project population dynamics of pond-breeding amphibian populations. We parameterized the model with life history and habitat data collected for the endangered Houston toad (Bufohoustonensis), a species for which there is a high degree of uncertainty for juvenile and adult male survival. During model evaluation, we focused on explicitly reducing this uncertainty, evaluating 16 different versions of the model that represented the range of parametric uncertainty for juvenile and adult male survival. Following POM protocol, we compared simulation results to four population-level patterns observed in the field: population size, adult sex ratio, proportion of toads returning to their natal pond, and mean maximum distance moved. Based on these comparisons, we rejected 11 of the 16 model versions. Results of the remaining versions confirmed that population persistence depends heavily on juvenile survival, and further suggested that probability of juvenile survival is likely between 0.0075 and 0.015 (previous estimates ranged from 0.003 to 0.02), and that annual male survival is near 0.15 (previous estimates ranged up to 0.43).     

Two-way coupling versus one-way forcing of plankton and fish models to predict ecosystem changes in the Benguela

‘End-to-end’ models have been adopted in an attempt to capture more of the processes that influence the ecology of marine ecosystems and to make system wide predictions of the effects of fishing and climate change. Here, we develop an end-to-end model by coupling existing models that describe the dynamics of low (ROMS–N₂P₂Z₂D₂) and high trophic levels (OSMOSE). ROMS–N₂P₂Z₂D₂ is a biogeochemical model representing phytoplankton and zooplankton seasonal dynamics forced by hydrodynamics in the Benguela upwelling ecosystem. OSMOSE is an individual-based model representing the dynamics of several species of fish, linked through opportunistic and size-based trophic interactions. The models are coupled through a two-way size-based predation process. Plankton provides prey for fish, and the effects of predation by fish on the plankton are described by a plankton mortality term that is variable in space and time. Using the end-to-end model, we compare the effects of two-way coupling versus one-way forcing of the fish model with the plankton biomass field. The fish-induced mortality on plankton is temporally variable, in part explained by seasonal changes in fish biomass. Inclusion of two-way feedback affects the seasonal dynamics of plankton groups and usually reduces the amplitude of variation in abundance (top-down effect). Forcing and coupling lead to different predicted food web structures owing to changes in the dominant food chain which is supported by plankton (bottom-up effect). Our comparisons of one-way forcing and two-way coupling show how feedbacks may affect abundance, food web structure and food web function and emphasise the need to critically examine the consequences of different model architectures when seeking to predict the effects of fishing and climate change.     

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.     

土壤甲胺磷抗性细菌种群特征脂肪酸生物标记的分析（Characteristics of PLFA Biomarkers for Acephatemet Resistant Bacteria Isolated from the Soils）

应用高浓度甲胺磷培养基,对农药厂排污口土壤微生物进行分离,鉴定出21株细菌,分属13个细菌属.对甲胺磷抗性细菌脂肪酸的分析,共测定到38个脂肪酸生物标记(PLFA),这些生物标记分为4种类型,即1)高频次分布的生物标记,普遍存在于细菌类群,属于细菌总体类群(Bacteriaingeneral)的生物标记;2)中频次分布的生物标记,在细菌种出现概率中等,可以用于代表细菌属类群(Bacteriumgenus)识别生物标记;3)低频次分布的生物标记,在细菌中的分布概率较小,可以用于指示特定细菌种间差异的生物标记;4)微频次分布的生物标记,这种生物标记仅在一种细菌种类出现,是细菌种特征生物标记.利用脂肪酸生物标记分析同属细菌不同种的差异,可将假单胞杆菌属分为2类,第1类包括了铜绿假单胞菌、荧光假单胞菌、丁香假单胞菌,其特征为17:0CYCLO生物标记含量小于3.60%;第2类包含了伞菌假单胞菌、威隆假单胞菌、恶臭假单胞菌、温哥华假单胞菌,其特征为17:0CYCLO生物标记含量大于5.99%.利用脂肪酸生物标记的差异对甲胺磷抗性细菌种的聚类分析,能有效地将细菌类群分为3类,微生物群落中存在着稳定的生物标记和受环境影响的生物标记,论文提供了一种脂肪酸生物标记的分析方法,结合细菌的生物学特性研究,可以解释微生物在环境中的变化,对于土壤微生物群落的研究具有重要意义.