Ecological Complexity最新文献

In 2012 Broom and Rychtar developed a new framework to consider the evolution of a population over a non-homogeneous underlying structure, where fitness depends upon multiplayer interactions amongst the individuals within the population played in groups of various sizes (including one). This included the independent model, and as a special case the territorial raider model, which has been considered in a series of subsequent papers. Here individuals are based upon the vertex of a graph but move to interact with their neighbours, sometimes meeting in large groups. The most important single property of such populations is the fixation probability, the probability of a single mutant completely replacing the existing population. In a recent paper we considered the fixation probability for the Birth Death Birth (BDB) dynamics for three games, a Public Goods game, the Hawk–Dove game and for fixed fitnesses for a large number of randomly generated graphs, in particular seeing if important underlying graph properties could be used as predictors. We found two good predictors, temperature and mean group size, but some interesting and unusual features for one type of graph, Barabasi–Albert graphs. In this paper we use a regression analysis to investigate (the usual) three alternative evolutionary dynamics (BDD, DBB, DBD) in addition to the original BDB. In particular, we find that the dynamics split into two pairs, BDB/DBD and BDD/DBB, each of which give essentially the same results and found a good fit to the data using a quadratic regression involving the above two variables. Further we find that temperature is the most important predictor for the Hawk–Dove game, whilst for the Public Goods game the group size also plays a key role, and is more important than the temperature for the BDD/DBB dynamics.

In mathematical modelling, several different functional forms can often be used to fit a data set equally well, especially if the data is sparse. In such cases, these mathematically different but similar looking functional forms are typically considered interchangeable. Recent work, however, shows that similar functional responses may nonetheless result in significantly different bifurcation points for the Rosenzweig–MacArthur predator–prey system. Since the bifurcation behaviours include destabilizing oscillations, predicting the occurrence of such behaviours is clearly important. Ecologically, different bifurcation behaviours mean that different predictions may be obtained from the models. These predictions can range from stable coexistence to the extinction of both species, so obtaining more accurate predictions is also clearly important for conservationists. Mathematically, this difference in bifurcation structure given similar functional responses is called structural sensitivity. We extend the existing work to find that the Leslie–Gower–May predator–prey system is also structurally sensitive to the functional response. Using the Rosenzweig–MacArthur and Leslie–Gower–May models, we then aim to determine if there is some way to obtain a functional description of data so that different functional responses yield the same bifurcation structure, i.e., we aim to describe data such that our model is not structurally sensitive. We first add stochasticity to the functional responses and find that better similarity of the resulting bifurcation structures is achieved. Then, we analyse the functional responses using two different methods to determine which part of each function contributes most to the observed bifurcation behaviour. We find that prey densities around the coexistence steady state are most important in defining the functional response. Lastly, we propose a procedure for ecologists and mathematical modellers to increase the accuracy of model predictions in predator–prey systems.

In order to meet the 2015 Paris Agreement for 1.5 °C global warming, per capita emissions need to come down to 2.9 tonnes by 2030. Food systems are known to be a significant source of an individual's carbon footprint and demand attention in sustainability management. The objective of this research is to conceptualise and define an intersection between contagion theory and socio-ecological systems models. This is achieved using a population dynamics model between two groups characterised by a distinct food regime: omnivores and vegans. The greenhouse gas emissions of each food regime is used to estimate the city's changing carbon foodprint as the food regimes shift by social contagion. Social contagion is identified as a catalyst for social tipping points, and emission pathways are explored with a variety of different contagion variables to test sensitivity towards a tipping point. The main finding is that the urban carbon foodprint can be reduced significantly with widespread adoption of veganism, but that the foodprint reaches a minimum at 1.97 tonnes CO₂-equivalent per capita. This demonstrates the need to embed food demand in urban climate governance such as nudging towards plant-based food alternatives. Nudging is discussed as a lever of ecological importance to social contagion. Lastly, socio-ecological contagion is defined as the interactions between social contagion and damage done to ecological systems to measure peer-to-peer spread of environmental stewardship agendas, such as the journey to Veganville.

The complex dynamics of a contagious disease in which populations experience horizontal and vertical transmissions, size variation, and virus mutations are of considerable practical and theoretical interest. We model such a system by dividing a population into three distinct groups: susceptibles ($S$), $C$-infected ($C$) and $F$-infected ($F$), based on the Susceptible-Infectious-Susceptible ($SIS$) model. Once the individuals in the $C$-infected group recover from the disease, they gain no permanent immunity. The virus can mutate in the group $C$. When it does, the individuals become members of the $F$-infected group. The mutated virus causes a lethal and incurable disease with a high mortality rate. We discuss the model for two cases. For the first case, all the newborns from infected mothers develop the disease shortly after their birth. For the second case, there exist equal transmission rates and the $C$-infected population is lifelong infectious. Our analysis shows that both systems have positive solutions, and the first model possesses four equilibrium points, the trivial one (extinction of the species), $C$-free equilibrium (extinction of the ancestor virus) and two endemic equilibria of different properties. We identify the net population growth rates of the susceptible and $C$-infected groups for the existence of the equilibria of the first model. We define the conditions of parameters for which species extinction and endemic equilibria are locally asymptotically stable. We observe that bifurcation occurs at the $C$-free equilibrium. For the second model, we find that there is only one endemic equilibrium and it is always locally asymptotically stable. We also determine the region for the net population growth rates of the susceptible and $F$-infected groups for the existence of the endemic equilibrium.

Unraveling the patterns of animals’ movements is crucial to understanding the basics of biogeography, tracking range shifts resulting from climate change, and predicting and preventing biological invasions. Many researchers have modeled animals’ dispersal under the assumptions of various movement strategies, either predetermined or directed by external factors, but none have compared the effects of different movement strategies on population survival and fitness. In this paper, using an agent-based model with a landscape divided into cells of varying quality, we compare the ecological success of three movement and habitat selection strategies (MHSSs): (i) Smart, in which animals choose the locally optimal cell; (ii) Random, in which animals move randomly between cells without taking into account their quality; (iii) Dreamer, in which animals attempt to find a habitat of dream whose quality is much higher than that of the habitat available on the map. We compare the short-term success of these MHSSs in good, medium and bad environments. We also assess the effect of temporal variation of habitat quality (specifically, winter harshness) on the success of each MHSS. Success is measured in terms of survival rate, dispersal distance, accumulated energy and quality of settled habitat. The most general conclusion is that while survival rate, accumulated energy and quality of settled habitat are affected primarily by overall habitat composition (proportions of different habitat types in the landscape), dispersal distance depends mainly on the MHSS. In medium and good environments, the Dreamer strategy is highly successful: it simultaneously outperforms the Smart strategy in dispersal distance and the Random strategy in terms of the other metrics.

The Tuta absoluta (Meyrick) (Lepidoptera, Gelechiidae), or tomato leafminer, is one of the most devastating pests of tomato crops in South America. The use of parasitoids and predators as biocontrol agents of this pest is an alternative to chemical insecticides and an environmentally safe strategy. However, an outcome of a three-way interspecific interaction (pest-parasitoid-predator) may bring surprises. The question is thus how a combination of enemies can contribute most effectively to reduction of the pest density. We examine this using an ordinary differential equations model. The formal and numerical analyses show that the joint use of both biological controls is the best option to decrease the tomato leafminer population, in contrast to the use of each biological control agent separately. Specifically, the numerical results show that in a scenario where the only biological control is given by the parasitoid, the introduction of the predator decreases the adult Tuta population by $28\%$, reducing even more the damage caused in tomato crops compared to the use of parasitoid alone.

Soil organic carbon is the largest carbon pool in the terrestrial biosphere. Large uncertainties exist in the numerical simulations of soil organic carbon due to inaccuracies in their mathematical descriptions of hydrological processes. In this study, the upper limit of uncertainty in modeled soil organic carbon that is induced by hydrological parameter errors, which may stem from measurement or experiential errors, is estimated in China under four different arid and humid conditions. The study was conducted using a conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach and a model of the coupled water-carbon cycle (the Lund-Potsdam-Jena Wetland Hydrology and Methane Dynamic Global Vegetation Model, LPJ-WHyMe). Uncertainties in hydrological processes resulted in the largest error (2.73 kg C m⁻² yr⁻¹, 20.2%) in the modeled soil organic carbon in the arid and semiarid regions of northern China, with errors of 1.20 kg C m⁻² yr⁻¹ (6.1%) in northeastern China, 0.45 kg C m⁻² yr⁻¹ (3.3%) in southern China, and -1.71 kg C m⁻² yr⁻¹ (13.7%) in the semihumid region of northern China. By analyzing the three components of soil organic carbon, the fast soil carbon pool was found to be the main cause of the uncertainties in modeled soil organic carbon in the four regions of China. Moreover, belowground litter was another cause of the uncertainties in the modeled soil organic carbon in northeastern China and in the semihumid region of northern China. Additional results indicated that the simulation and prediction abilities of soil organic carbon could be improved by reducing parameter errors in hydrological processes through observations or targeted observations. The parameter sensitivity test showed that the benefits to modeling soil organic carbon were similar when reducing the errors in the sensitive hydrological parameter subset, compared to the benefits of reducing the errors in all the hydrological parameters.

Weekly averaged datasets from fourteen AmeriFlux ecosystem monitoring sites spread across the Americas, processed to the FLUXNET2015 standard, are statistically evaluated to characterize their seasonal net ecosystem exchange (NEE) trends. The sites cover wetland, cropland, woodland, grassland and tundra ecosystems. Up to twenty measured variables from the sites are variously correlated with NEE. A comparison of Pearson and Spearman correlation coefficients reveals that the variables are behaving parametrically with respect to NEE for the wetland, woodland (two out of three sites) and tundra locations, but non-parametrically for cropland and grassland sites. Multi-linear regression (MLR) analysis also distinguishes those ecosystems. MLR predicted versus calculated NEE follow Y ≈ X relationships for the wetland and tundra sites, whereas for the other ecosystems the MLR results follow Y≠X trends. Moreover, the coefficient values of the MLR optimum solutions for each ecosystem reveal quite distinct relative influences of the measured variables on the NEE predicted values. These results imply that NEE at wetland and tundra sites can be relatively easily predicted from the FLUXNET2015 set of recorded variables. On the other hand, the other three types of ecosystem sites cannot be easily predicted from those variables, implying that other factors substantially influence NEE at those sites.

Interactions with pollinators underlie the structure and function of plant communities. Network analysis is a valuable tool for studying plant-pollinator interactions, but these networks are most frequently built by aggregating interactions at the species level. Interactions are between individuals and an advantage of individual-based networks is the ability to integrate inter-individual variation in traits and environmental context within complex ecological networks. We studied the influence of inter-individual variation on pollinator sharing among foundation shrubs and cactus in a desert ecosystem using plant individual-based pollinator visitation networks. We hypothesized that the traits that alter attractiveness of plants to pollinators will also influence an individual plant's role within the visitation network. Foundation plants growing with higher densities of nearby blooming shrubs had higher pollinator visitation rates and had greater access to the conspecific mating pool, suggesting widespread and diffuse pollination facilitation within this community. Further, shrub density influenced the role of betweenness centrality and the effective number of partners (e^H). Floral display size also influenced the effective number of interaction partners but did not directly influence the centrality measures for individual plants or other measures of network structure despite increasing visitation rates. The individual-based visitation networks were significantly modular and module membership was predicted by species identity and pollinator visitation rates. Ecological and individual context mediate the outcome of pollinator-mediated interactions and are fundamental drivers of whole community structure. This study shows that the density of immediate neighbours can influence the overall structure of plant-pollinator interaction networks. Exploring the contribution of intraspecific variation to community interaction networks will improve our understanding of drivers of community-level ecological dynamics.

This paper presents a conceptual framework for analyzing forest complexity as the combination of the variety of species and key structures that are associated with the composition, structure, and function of forest stands. Several spatial indicators have been developed to characterize the biodiversity, the structural complexity, and anthropogenic effects that can be observed in Mexican forests. By integrating several stand complexity attributes, the forest condition can be characterized as a function of species composition, stand structural attributes, and forest development. In addition, indicators of anthropogenic effects were also analyzed to identify their influence on forest eco-complexity, and therefore, on the current condition of forests. The results of applying this conceptual framework showed that Mexican forest are ecologically complex, with varying levels of anthropogenic impacts that modify the structural forest characteristics, particularly in tropical forests. The main factor explaining the current eco-complexity condition in tropical forests was associated with early stages of forest development, due to ecological degradation, and showed a generalized loss of attributes, particularly for stand complexity and stand development. In contrast, temperate forests exhibited better eco-complexity conditions, especially for those attributes that define forest stand occupancy and development. Mining activities, forest extraction as selective harvesting, forest fires, land use change, and road openings are critical human activities that directly affect forest structure and, ultimately, modify forest eco-complexity and integrity. This eco-complexity index derived for Mexican forests can be used to integrate measures of forest structure and functioning, and thereby better inform decision making and policy development.