Testing the impact of social forces on the evolution of Sahelian farming systems: A combined agent-based modeling and anthropological approach

This article presents the results of a methodology based on an extensive sociological fieldwork in three different sites settled along a gradient of aridity in Nigerien Sahel. This fieldwork led to build a set of rules for the behaviour of individuals in non-pastoralist villages. We implemented these rules into an agent-based model simulating three village archetypes. Each archetype includes biophysical, economical, social agricultural and livestock modules. Results from simulations with no social transition processes show that villages specialize themselves into different economic activities according to natural resource specificities: A decreasing intensification gradient is observed from the most favoured site, with more local productions and good ecological indicators, to the less-favoured site, with a growing proportion of the population wealth coming from migration remittances and “off-shore” livestock. Two family transition processes were implemented, following field observations and literature-based hypotheses: family organizations evolve between a patriarchal mode and a non-cooperative mode following tensions due to income redistribution. Family inheritance systems evolve from a “customary” one-heir mode to a “local Muslim” mode in which all males inherits land. This evolution depends on family tensions due to land availability. Once introducing these processes, the population of each site differentiates itself into specialized groups according to size, assets and social status. Meanwhile, the group proportions and specializations strongly vary according to the sites but they are all characterized by the emergence of individualistic family types and the increase of the village populations’ robustness.     

Analysis of behavioral changes of zebrafish (Danio rerio) in response to formaldehyde using Self-organizing map and a hidden Markov model

Two computational methods were applied to classification of movement patterns of zebrafish (Danio rerio) to elucidate Markov processes in behavioral changes before and after treatment of formaldehyde (0.1 mg/L) in semi-natural conditions. The complex data of the movement tracks were initially classified by the Self-organizing map (SOM) to present different behavioral states of test individuals. Transition probabilities between behavioral states were further evaluated to fit Markov processes by using the hidden Markov model (HMM). Emission transition probability was also obtained from the observed variables (i.e., speed) for training with the HMM. Experimental transition and emission probability matrices were successfully estimated with the HMM for recognizing sequences of behavioral states with accuracy rates in acceptable ranges at central and boundary zones before (77.3-81.2%) and after (70.1-76.5%) treatment. A heuristic algorithm and a Markov model were efficiently combined to analyze movement patterns and could be a means of in situ behavioral monitoring tool.     

Modeling and inference of animal movement using artificial neural networks

Movement of animals in relation to objects in their environment is important in many areas of ecology and wildlife conservation. Tools for analysis of movement data, however, still remain rather limited. In previous work, we developed nonlinear regression models for movement in relation to a single landscape feature. Here we greatly expand these previous models by using artificial neural networks. The new models add substantial flexibility and capabilities, including the ability to incorporate multiple factors and covariates. We devise a likelihood-based model fitting procedure that utilizes genetic algorithms and demonstrate the approach with movement data for red diamond rattlesnakes. The proposed methodology can be useful for global positioning system tracking data that are becoming more common in studies of animal movement behavior.     

Groups confuse predators by exploiting perceptual bottlenecks: a connectionist model of the confusion effect

Aggregation is a well documented behaviour in a number of animal groups. The confusion effect is one mechanism thought to mitigate the success of predators feeding on gregarious prey and hence favour aggregation. An artificial neural network model of prey targeting is developed to explore the advantages prey species might derive through a tendency to group. The network illustrates how an abstract model of the computational mechanisms mediating the perception of prey position is able to show a degradation in performance as group size increases. The relationship between group size and predator confusion has a characteristic decreasing decelerating shape. Prey oddity is shown to reduce the impact of the confusion effect, thereby allowing predators to target prey more accurately. Hence shoaling behaviour is most profitable to the prey when prey phenotypes are visually indistinguishable to a predator. Futhermore it is shown that prey oddity is relatively more costly in large groups than in small groups and the implications for assortative schooling are discussed. Both the model and the results are intended to make the general point that cognitive constraints will limit the information that a nervous system can process at a number of different levels of neural organization.     

Agrodiversity v.2: An educational simulation tool to address some challenges for sustaining functional agrodiversity in agro-ecosystems

Functional agrodiversity can be useful and even essential for, i.e., the long-term sustainability of agriculture. However, still many aspects of this concept are not well understood. The interplay between species in diverse agro-ecosystems is based on processes as, i.e., competition, facilitation, and predator-prey relations. The net-effect of these processes on crop growth is not static and can change over time as the relative density of species change. The equilibrium state of a diverse agro-ecosystem might be far from optimum or even unproductive. This makes agrodiveristy a concept which is not easily grasped nor obtained or maintained. We believe that an agent-based model can facilitate learning on the topic of functional agrodiversity. In this paper, we present the agent-based simulation model, Agrodiversity v.2, developed in Netlogo 3.1.5. The model simulates a virtual diverse agro-ecosystem with four ecological agents. The user is challenged to explore ecological parameters and design a productive sustainable system. The model's “simplest playing level” shows that a proper balance between the co-existing species is necessary so that their ecological interactions allow the multi-species system to become self-organized and persist over time. It demonstrates the transient nature of profitable functional agrodiversity. Our analysis on the effects of using Agrodiversity v.2 on actual learning shows that the learning took place. Students increased the quality of their answers to paper-based individual questions on the topic from 29% during passive/conceptual teaching to 86% after the simulation session. On average students stated to have learnt 55% of their current knowledge through the workshop of which 76% was learnt by using the simulation.     

Vigilance for predators: detection and dilution effects

Summary Grouped individuals are less vigilant for predators than solitary conspecifics because (1) grouping increases the likelihood of predator detection (detection effect) and (2) grouping makes it less likely that any given individual will be preyed upon (dilution effect). However, many models of vigilance behaviour consider only the detection effect, and the interaction of the two effects has been insufficiently considered. I present two models of vigilance behaviour and test them using data on the vigilance of elk, Cervus elaphus. The first model, based on the detection effect alone, is implicit in many published formulations of the relationship between vigilance and group size. Although it predicts the direction of the relationship between vigilance and group size, it provides a poor explanation of the form of the relationship. The second model incorporates both detection and dilution effects. Regression analysis on this security model indicates that the model provides a good prediction of both the direction and the form of the relationship between vigilance and group size, explaining 69% of the variance in vigilance frequency. The security model demonstrates that both detection and dilution are important in determining the frequency of vigilance behaviour but that the relative importance of these two effects changes across group size, with detection providing relatively less benefit as group size increases. However, even when groups are large, individuals should exhibit at least some vigilance because although dilution alone provides much protection from predation, even a low level of vigilance greatly increases the likelihood that an individual will survive repeated predation attempts.     

Behavioural switching in a central place forager: patterns of diving behaviour in the macaroni penguin (Eudyptes chrysolophus)

Recording the activity of animals as they migrate or forage has proven hugely advantageous to understanding how animals use their environment. Where animals cannot be directly observed, the problem remains of how to identify distinct behaviours that represent an animal’s decision-making process. An excellent example of this problem is that of foraging penguins, which travel to sea to find prey to provision their young. Without direct sampling of the prey field, we cannot calibrate patterns of movement with prey capture, and therefore we cannot determine how different activities link to decision-making. To overcome this, we use a hidden markov model (HMM), which is a machine-learning technique that seeks to identify the underlying states of a system from observable outputs. We apply HMM to determine classes of behaviour from repetitive dives. We take dive data from 103 breeding macaroni penguins at Bird Island, South Georgia, for which we have measures of weight gain over a trip. We identify two classes of behaviour; those of short-shallow and long-deep dives. Using these two behaviours, we calculate the transition probabilities between these states and analyse these data to determine what predicts variation in the transition probabilities. We found that the stage of reproduction during a season, the sex and year of an individual influenced the probability of transition between long-deep and short-shallow sequential dives. We also found differences in the hourly transition rates between the four reproductive stages (incubation, broodguard, crèche and premoult) over a daily cycle. We conclude that this application of HMMs for behavioural switching is potentially useful for other species and other types of recorded behaviour.     

Changes of movement patterns from early dispersal to settlement

Moving and spatial learning are two intertwined processes: (a) changes in movement behavior determine the learning of the spatial environment, and (b) information plays a crucial role in several animal decision-making processes like movement decisions. A useful way to explore the interactions between movement decisions and learning of the spatial environment is by comparing individual behaviors during the different phases of natal dispersal (when individuals move across more or less unknown habitats) with movements and choices of breeders (who repeatedly move within fixed home ranges), that is, by comparing behaviors between individuals who are still acquiring information vs. individuals with a more complete knowledge of their surroundings. When analyzing movement patterns of eagle owls, Bubo bubo, belonging to three status classes (floaters wandering across unknown environments, floaters already settled in temporary settlement areas, and territory owners with a well-established home range), we found that: (1) wandering individuals move faster than when established in a more stable or fixed settlement area, traveling larger and straighter paths with longer move steps; and (2) when floaters settle in a permanent area, then they show movement behavior similar to territory owners. Thus, movement patterns show a transition from exploratory strategies, when animals have incomplete environmental information, to a more familiar way to exploit their activity areas as they get to know the environment better.     

Application of an evolutionary algorithm to the inverse parameter estimation of an individual-based model

Inverse parameter estimation of individual-based models (IBMs) is a research area which is still in its infancy, in a context where conventional statistical methods are not well suited to confront this type of models with data. In this paper, we propose an original evolutionary algorithm which is designed for the calibration of complex IBMs, i.e. characterized by high stochasticity, parameter uncertainty and numerous non-linear interactions between parameters and model output. Our algorithm corresponds to a variant of the population-based incremental learning (PBIL) genetic algorithm, with a specific “optimal individual” operator. The method is presented in detail and applied to the individual-based model OSMOSE. The performance of the algorithm is evaluated and estimated parameters are compared with an independent manual calibration. The results show that automated and convergent methods for inverse parameter estimation are a significant improvement to existing ad hoc methods for the calibration of IBMs.     

Dynamic compartment approach for modelling regimes of carbon cycle functioning in bog ecosystems

Svirezhev's method of dynamic model design by a given “storage-flow” diagram [Svirezhev Y.M., 1997. On some general properties of trophic networks. Ecol. Model. 99, 7–17] is developed and used for investigating dynamic regimes of carbon cycle functioning in a typical boreal transitional bog ecosystem. Ecosystems are often represented by static “storage-flow” diagrams reflecting their structure and matter or energy transfer between components at fixed time moments. Using the data of such diagrams aggregated in ecological field studies one can construct a dynamic model of the ecosystem to predict its future behaviour and to estimate a response to external perturbations—natural and human. Stability of both current equilibrium and possible alternative steady states and more complicated attractors are studied under two types of parameter perturbation: CO₂ atmospheric concentration increase initiated by greenhouse effect, and change in the rate of carbon output from dead organic matter and litter which depends on the water table level and possible peat excavation. Calculation of bifurcation curves gives areas in the parameter space where stable functioning of carbon cycle is provided. Steady states can be interpreted as raised bog, meadow, forest and fen. CO₂ concentration increase leads the current state of transitional bog to loose stability with appearance of oscillatory dynamics and further evolution to the chaotic attractor. The model is rich by chaotic solutions serving as transition regimes between regular steady and periodic attractors. Another chaotic regime is formed from forest equilibrium and exists in the same area of phase space where current equilibrium is stable.     

Projecting landscape changes in southern Chile: Simulation of human and natural processes driving land transformation

We describe a simulation model representing the most important human and natural factors driving land use and cover changes (LUCC) in southern Chile. We evaluate the model by examining its ability to simulate LUCC observed over the past three decades, conduct a sensitivity analysis of simulated trends to changes in important model parameters, and use the model to project likely landscape transformations over the next decade under “as usual,” “pessimistic,” and four “optimistic” scenarios. The model consists of five submodels representing LUCC on two distinct soil formations (volcanic ash and gleysols) and four major land use categories: native forest, agricultural land, shrubland, and urban land. Land use and cover sub-categories include old growth forests, secondary forests, and low and flooded shrubland. The model simulated well general historic trends in forest cover, agricultural land, shrubland, and urban land: from a forest-dominated landscape in 1976 to a landscape dominated by shrubland and agricultural land by 2007. Forest loss, forest degradation by logging and clearing for agriculture were the most important direct drivers of LUCC: forest logging and clearing were most important from 1976 to 1985, whereas after 1985 logging for firewood, driven by population growth, was most important. Sensitivity analysis indicated that model projections of general trends in the main land use and cover categories were not overly sensitive to changes in important model parameters, although further study is necessary to improve our estimates of the proportion of pasture requirements supplied by clearing low shrubland. Projections of LUCC suggested that a reduced amount of secondary forest would be left by 2017 if no actions are taken to reduce forest loss (“as usual”). Increasing population (“pessimistic scenario”) resulted in similar trajectories than those predicted by the as usual scenario, whereas reducing logging for firewood and increasing forest recruitment from shrubland could reduce loss of native forest by nearly one-third (“optimistic scenarios”). Surprisingly, shrubland exhibited the most complex and influential dynamics in all scenarios, being the immediate outcome of forest loss and the main long-term source of land for agriculture, urban expansion, and forest recovery. Few studies in Chile, or elsewhere, have considered the importance of this intermediate successional stage. Of the scenarios simulated, financial incentives targeted toward channeling shrubland into regenerated forest seemed most promising, although obstacles to such a management strategy exist.     

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).     

Prediction of the Transport and Dispersal of Oil in the South Caspian Sea Resulting from Blowouts

A 3-D hybrid flow/transport model is developed to predict the dispersal of oil pollution in coastal waters. The transport module of the model takes predetermined current and turbulent diffusivities and uses Lagrangian tracking to predict the motion of individual particles (droplets), the sum of which constitutes a hypothetical oil spill. Currents and turbulent diffusivities used in the model are generated by a numerical ocean circulation model (POM) implemented for the Caspian Sea. The basic processes affecting the fate of the oil spill are taken into account and parameterised in the transport model. The process of evaporation is modeled with the pseudo-component approach. The model is implemented for a simulated continuous release in the coastal waters of the south part of the Caspian Sea. Numerical experiments simulate 5- and 10-day blowout scenarios resulting from sources situated in areas were intensive and extensive development of oil deposits is expected soon. Oil slick movement and risk of coastline contamination by beaching of offshore oil spills are illustrated for different wind conditions.     

Establishment patterns in a secondary tree line ecotone

On semi-open pre-alpine fen pastures Alder encroachment creates a dynamic mosaic of grassland and woodland, which is rich in ecotones from fen to Carr. The structural diversity in colonisation patterns of Alder on fens suggests a dependency on multiple environmental drivers. Unidirectional progressive ecotone development provides an opportunity to address a current deficit in understanding successional patterns, i.e. process-pattern relationships in a multiple factor regime.We developed an individual-based model of Alder establishment on fen grassland to investigate the dependency of encroachment patterns upon seed production, dispersal distances and safe site availability. The purpose of the model is to provide a causal understanding of establishment patterns of Alder. In the model, all life processes of Alder individuals were parameterised with field data. This allowed us to strictly perform bottom-up simulations and successfully check plausibility by comparing simulated establishment patterns of cohorts with observed ecotone structures.Simulation results show that establishment patterns strongly depend on environmental drivers. Spatial progression of Alder encroachment and width of ecotones, respectively, mainly depend on wind speed during seed dispersal. Dense establishment of Alder leading to community change from fen grassland to Carr, requires windows of opportunity, which are defined by the rare coincidence of widespread dispersal, high seed production and favorable establishment conditions. Life-history traits of Alder (mast year cycle, high seed weight, weak establishment in fen) spatially and temporarily constrain the encroachment process. The structural diversity of long-term encroachment patterns is explained by the event-driven character of encroachment.Modelling individual establishment pathways of seedlings starting from germination revealed an endogenous stochasticity in establishment patterns emerging from low seed densities in the tail of the dispersal function. We conclude an inherent stochastic structure of dispersal-limited tree line ecotones, which limits reconstruction of processes from patterns.In order to describe long-term successional patterns of Alder encroachment at landscape scale, we propose the combination of two concepts: deterministic “patch-movement” of Alder woodland driven by continuous ecotone migration together with rare and stochastic “infiltration” of single Alder trees into open fen grasslands. Conservation management can control predictable “patch-movement” by cutting off maturing saplings around existing Alder woods. But the preservation of the actual large proportion of open grassland in fen pastures from infiltrating Alder seedlings and from the subsequent shift of the pasture to a densely wooded state would require mowing additionally to extensive grazing.     

Spatial prediction of soil contamination based on machine learning: a review

● A review of machine learning (ML) for spatial prediction of soil contamination. ● ML have achieved significant breakthroughs for soil contamination prediction. ● A structured guideline for using ML in soil contamination is proposed. ● The guideline includes variable selection, model evaluation, and interpretation. Soil pollution levels can be quantified via sampling and experimental analysis; however, sampling is performed at discrete points with long distances owing to limited funding and human resources, and is insufficient to characterize the entire study area. Spatial prediction is required to comprehensively investigate potentially contaminated areas. Consequently, machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution. The characteristics, advantages, and applications of machine learning models used to predict soil pollution are reviewed in this study. Satisfactory model performance generally requires the following: 1) selection of the most appropriate model with the required structure; 2) selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability; 3) improvement of model reliability through comprehensive model evaluation; and 4) integration of geostatistics with the machine learning model. With the enrichment of environmental data and development of algorithms, machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.     

Pattern-oriented modeling of bird foraging and pest control in coffee farms

We develop a model of how land use and habitat diversity affect migratory bird populations and their ability to suppress an insect pest on Jamaican coffee farms. Bird foraging—choosing which habitat patch and prey to use as prey abundance changes over space and time—is the key process driving this system. Following the “pattern-oriented” modeling strategy, we identified nine observed patterns that characterize the real system's dynamics. The model was designed so that these patterns could potentially emerge from it. The resulting model is individual-based, has fine spatial and temporal resolutions, represents very simply the supply of the pest insect and other arthropod food in six habitat types, and includes foraging habitat selection as the only adaptive behavior of birds. Although there is an extensive heritage of bird foraging theory in ecology, most of it addresses only the individual level and is too simple for our context. We used pattern-oriented modeling to develop and test foraging theory for this across-scale problem: rules for individual bird foraging that cause the model to reproduce a variety of patterns observed at the system level. Four alternative foraging theories were contrasted by how well they caused the model to reproduce the nine characteristic patterns. Four of these patterns were clearly reproduced with the “null” theory that birds select habitat randomly. A version of classical theory in which birds stay in a patch until food is depleted to some threshold caused the model to reproduce five patterns; this theory caused lower, not higher, use of habitat experiencing an outbreak of prey insects. Assuming that birds select the nearby patch providing highest intake rate caused the model to reproduce all but one pattern, whereas assuming birds select the highest-intake patch over a large radius produced an unrealistic distribution of movement distances. The pattern reproduced under none of the theories, a negative relation between bird density and distance to trees, appears to result from a process not in the model: birds return to trees at night to roost. We conclude that a foraging model for small insectivorous birds in diverse habitat should assume birds can sense higher food supply but over short, not long, distances.     

Models of withdrawing renewable and non-renewable resources based on Odum's energy systems theory and Daly's quasi-sustainability principle

This paper presents a theoretical model of withdrawing resources based on Odum's energy systems diagrams. According to the theory of a general pulsing principle, withdrawing resources changes in time shifting from the initial phases of growth and maturity to the phases of descent and low energy restoration. A simulation was performed in order to hypothesize potential future trends of withdrawing renewable and non-renewable resources and to show some aspects of their sustainability–unsustainability, respectively. According to Odum's theory, after the rapid growth of the last century, our civilization is living in a climax transition phase and it is now approaching a descent phase. A “way down” will be necessary due to the exhaustion of non-renewable and to the limited use of renewable resources. An integrated “renew–non-renew” model was developed by Odum to show how a “business as usual” trend will bring us to a drastic transition to a world that uses scarce renewable resources. Nevertheless, a different choice is possible, based on Daly's concept of quasi-sustainability that can inspire a new model. The latter argued that the exploitation of a non-renewable resource must be paired with a compensating investment in a renewable substitute. Our model shows that we can use non-renewable resources better to considerably improve our capacity of capturing renewable resources in the future. We present this as a necessary condition to address a sustainable environmental policy.     

Information transfer and gain in flocks: the effects of quality and quantity of social information at different neighbour distances

We assessed experimentally how the quality and quantity of social information affected foraging decisions of starlings (Sturnus vulgaris) at different neighbour distances, and how individuals gained social information as a function of head position. Our experimental set up comprised three bottomless enclosures, each housing one individual placed on a line at different distances. The birds in the extreme enclosures were labelled senders and the one in the centre receiver. We manipulated the foraging opportunities of senders (enhanced, natural, no-foraging), and recorded the behaviour of the receiver. In the first experiment, receivers responded to the condition of senders. Their searching rate and food intake increased when senders foraged in enhanced conditions, and decreased in no-foraging conditions, in relation to natural conditions. Scanning was oriented more in the direction of conspecifics when senders behaviour departed from normal. In the second experiment, responses were dose dependent: receivers increased their searching rate and orientated their gaze more towards conspecifics with the number of senders foraging in enhanced food conditions. In no-foraging conditions, receivers decreased their searching and intake rates with the number of senders, but no variation was found in scanning towards conspecifics. Differences in foraging and scanning behaviour between enhanced and no-foraging conditions were much lower when neighbours were separated farther. Overall, information transfer within starling flocks affects individual foraging and scanning behaviour, with receivers monitoring and copying senders behaviour mainly when neighbours are close. Information transfer may be related to predation information (responding to the vigilance of conspecifics) and foraging information (responding to the feeding success of conspecifics). Both sources of information, balanced by neighbour distance, may simultaneously affect the behaviour of individuals in natural conditions.Communicated by H. Kokko     

Inheritance of dominance in honeybees (Apis mellifera capensis Esch.)

Summary Hierarchies in worker dominance are well developed in reproductive workers of the Cape honeybee (Apis mellifera capensis Esch.). Elements that influence worker dominance (trophallactic behaviour, fecundity, queen substance content) are mainly genetically determined. The estimated values for the heritabilities in the broader sense range from 0.27 to 0.89. This genetically based hierarchy leads to new insight into the theory of natural selection in honeybees. Besides selection at the colony level, or selection at the level of reproductives, natural selection also operates at the level of the individual worker bee within a colony.     

Cooperative hunting in lions: the role of the individual

Summary Individually identified lions (Panthera leo) were observed on the open, semi-arid plains in Namibia. Data from 486 coordinated group hunts were analysed to assess cooperation and individual variation in hunting tactics. Group hunts generally involved a formation whereby some lionesses (wings) circled prey while others (centres) waited for prey to move towards them. Those lionesses that occupied wing stalking roles frequently initiated an attack on the prey, while lionesses in centre roles moved relatively small distances and most often captured prey in flight from other lionesses. Each lioness in a given pride repeatedly occupied the same position in a hunting formation. Hunts where most lionesses present occupied their preferred positions had a high probability of success. Individual hunting behaviour was not inflexible, however, but varied according to different group compositions and to variations in the behaviour of other individuals present. The role of cooperative hunting and its apparent advantages within the semi-arid environment of Etosha National Park, Namibia, are discussed.