Herding tendency as an aggregating factor in a binary mixture of social entities

The role of herding tendency in the group formation of social entities is hereby explored. The herding tendency is quantified by a parameter α∈[0,1]$\alpha \in [0\text{,}1]$. The system consists of a mixture of two types of entities: (i) those with α>0$\alpha >0$ and (ii) those with α=0$\alpha =0$. The latter consist a fraction p   of the entire population. The dynamics of agent interactions leads to the formation of clusters of different sizes. The size distribution D(s)$D(s)$ of these clusters are found to obey a power-law only in the limit that α→1$\alpha \to 1$ and p→0$p\to 0$. Group-size data of several real-world animal systems are fitted with curves generated by the model. This study contributes further to the understanding of group-forming behavior commonly cited in ecological studies.     

Description and sensitivity analysis for the LESV model: Water quality variables and the balance of organisms in a fjordic region of restricted exchange

In this paper we describe a new ecological model for Regions of Restricted Exchange (RRE), such as fjords, estuaries, rias and lagoons. The model is intended to simulate the impact of external nutrient input on microplankton (phytoplankton plus pelagic microheterotrophs) in RREs. We have implemented the model with the practical purpose of finding a safe limit to the capacities of RRE to assimilate fish-farm waste. Sea-cage farming of fish is increasing in fjords in northern and southern hemispheres, and its external nutrient input can lead to environmental problems such as eutrophication and deoxygenation. The model includes a physical system of three layers with exchanges driven by tidal movement, freshwater input, wind stirring. The biological part includes two microplankton compartments, each parameterizing a microbial loop and each containing chlorophyll. The first compartment represents diatoms and associated heterotrophs, and the second compartment represents flagellates and associated heterotrophs. As well as the balance of these organisms, the model simulates concentrations of nutrient N, P, and Si, dissolved oxygen, and water transparency. Chlorophyll and nutrient change are linked by yields (q  ). Losses of microplankton to grazing by mesozooplankton or benthos are simulated by a temperature-dependent grazing pressure acting on a mean loss (_L0)$(L_0)$. The model also includes the ability to simulate point source inputs of nutrients or organic matter and a generic tracer with first order decay. Sea-cage fish-farms exemplify such point sources. In order to explore model behaviour, we included inputs from a 1500 tonnes salmon farm multiplied by a factor (γ)$(\gamma )$. We carried out sensitivity analysis to identify the most influential model parameters and forcing variables in the case of the shallow Scottish fjord, Loch Creran, in 1975 before the introduction of salmon farming. We tested the model fit to this pristine state (γ=0)$(\gamma =0)$, using Major Axis Regression of simulated variables on observed variables. The model successfully follows the seasonal cycles of chlorophyll (summer over both microplanktons) and the limiting nutrients (P, N). The sensitivity analysis identified three sets of key parameters: (γ)$(\gamma )$ and other fish-farm coefficients, which control farm waste effects on an RRE; (_L0)$(L_0)$ parameters for each microplankton, which link these to the rest of the ecosystem and which have implications for future inclusion of shellfish farming in the model and, chlorophyll yields from nutrients (q), which are crucial for the predication of eutrophication and the ecological understanding of the model.     

Eventual saturation of the climate–carbon cycle feedback studied with a conceptual model

A saturation of climate–carbon cycle feedback was found earlier in the simulations with the IAP RAS climate model of intermediate complexity. Here, this eventual saturation is interpreted by using a conceptual linearised coupled model. It is shown that this saturation is due to weak, logarithmic, dependence of the carbon dioxide radiative forcing on its atmospheric concentration. This eventual saturation leads to the non-monotonic behaviour of climate–carbon cycle parameter f   in time. If the time scale of the atmospheric CO₂${\text{CO}}_2$ build up is _tp$t_{\text{p}}$ then, starting from an initial equilibrium, f   approaches maximum in time ?_tp$?t_{\text{p}}$. Afterwards, climate–carbon cycle parameter decreases and eventually tends to unity. The time scale of the latter decrease is _t1=(1−5)_tp$t_1=(1-5)t_{\text{p}}$. A dependence of _tm$t_{\text{m}}$ and _t1$t_1$ on governing parameters of the conceptual model is studied. It is argued that an eventual saturation of the climate–carbon cycle feedback is expected to occur also in the other integrations of sufficient length with coupled climate–carbon cycle models.