Fuzzy cognitive maps outmatch loop analysis in dynamic modeling of ecological systems

Modeling natural systems is challenging due to their complexity in terms of variables, interactions, and dynamics. Much of this complexity is rooted in the existence of multiple ways through which acting variables affect each other. Besides the simple direct effects, numerous indirect effects emerge in ecological systems. Through an illustrative example, I exemplify here several advantages of fuzzy cognitive maps (FCM) over loop analysis (LA) in dynamic modeling of ecological systems. In addition to being able to incorporate information about the magnitude of variables interactions, FCM can make predictions about multiple simultaneous perturbations. Furthermore, FCM allow for the simulation of different magnitude of initial perturbations to the system. Last, FCM estimate the amount of variable increase/decrease, not just the likely direction of change. Hence, even if LA is still much more used than FCM in the scientific literature, FCM can be considered fitter than LA in modeling ecological systems

Some thoughts on the control of network systems

The controllability of network-like systems is becoming a trendy key-issue in many disciplines, including ecology and biology. To control a biological, ecological or economic system is to make it behave according to our wishes, at the least possible cost. In this paper, I propose some ideas on networks control that do not precisely follow recent papers on the argument. By the way, since this scientific topic is still in open evolution, discordant thoughts might be helpful to the debate

Some steps forward in semi-quantitative networks modelling

System dynamics is an umbrella term for those approaches aiming to understand the behaviour of network-like systems over time. What makes system dynamics different from other methods about complex systems is the use of feedback loops, stocks and ?ows which allow to model how network-like systems can display strong nonlinear behaviours. Fuzzy Cognitive Maps (FCM) are semi-quantitative networks that can be regarded as a system dynamics method. Here I suggest 4 kinds of modifications to FCM: (1) if-then-else stop option; (2) piecewise option; (3) non-monotonicity option; (4) non-linearity option. These improvements to FCM might allow for fitter simulations of ecological and biological systems over time. I also present an applicative example to illustrate the proposed modifications

A new fuzzy algorithm for ecological ranking

Ecological ranking is a prerequisite to many kinds of environmental decisions. It requires a set of 'objects' (e.g., competing sites for species reintroduction, or competing alternatives of environmental management) to be evaluated on the basis of multiple weighted criteria, and then ranked from the best to the worst, or vice versa. The resulting ranking is then used to choose the course of an action (e.g., the optimal sites where a species can be reintroduced, or the optimal management scenario for a protected area). In this work, a new tool called FuzRnk is proposed as a modification of classic fuzzy algorithm. FuzRnk, which is freely available upon request from the author, allows for a fuzzy ranking of GIS objects (e.g., landscape patches or zones within protected areas). With respect to classic fuzzy algorithm, FuzRnk introduces two modifications: a) criteria can be weighted on the basis of their importance, b) not only the best performances, but also the worst ones are considered in the ranking procedure

Computing the uncertainty associated with the control of ecological and biological systems

Recently, I showed that ecological and biological networks can be controlled by coupling their dynamics to evolutionary modelling. This provides numerous solutions to the goal of guiding a system's behaviour towards the desired result. In this paper, I face another important question: how reliable is the achieved solution? In other words, which is the degree of uncertainty about getting the desired result if values of edges and nodes were a bit different from optimized ones? This is a pivotal question, because it's not assured that while managing a certain system we are able to impose to nodes and edges exactly the optimized values we would need in order to achieve the desired results. In order to face this topic, I have formulated here a 3-parts framework (network dynamics - genetic optimization - stochastic simulations) and, using an illustrative example, I have been able to detect the most reliable solution to the goal of network control. The proposed framework could be used to: a) counteract damages to ecological and biological networks, b) safeguard rare and endangered species, c) manage systems at the least possible cost, and d) plan optimized bio-manipulations

From Ecosystem Ecology to Landscape Ecology: a Progression Calling for a Well-founded Research and Appropriate Disillusions

In this paper, 1) a delineation of main theoretical, methodological and applicative issues of landscape ecology, 2) a comparison between landscape and ecosystem ecology, 3) a critical overview of actual limits of landscape ecology, are depicted. We conclude that: a) from a theoretical viewpoint, ecosystem and landscape ecology differ since they deal with ecological topics having very different spatial and temporal scales, b) from a practical standpoint, they deal with dissimilar purposes emerging both from unlike research scales and different approaches, as the interest of landscape ecology is mainly focused on the whole ecological mosaic rather than on single components, in this view assuming an "horizontal" ecological perspective, c) transdisciplinarity is still a work in progress in landscape ecology, d) several research purposes in landscape ecology are far from being reached, e) a bridge lacks between the "horizontal" perspective adopted from landscape ecology and the "vertical" approach distinctive of ecosystem ecology, therefore, they actually behave as detached disciplines. However, in our vision, landscape ecology contains the seeds for becoming a self-contained scientific discipline as well as the interface among the distinct sectors of environmental research and planning

Detecting ecological breakpoints: a new tool for piecewise regression

Simple linear regression tries to determine a linear relationship between a given variable X (predictor) and a dependent variable Y. Since most of the environmental problems involve complex relationships, X-Y relationship is often better modeled through a regression where, instead of fitting a single straight line to the data, the algorithm allows the fitting to bend. Piecewise regressions just do it, since they allow emphasize local, instead of global, rules connecting predictor and dependent variables. In this work, a tool called RolReg is proposed as an implementation of Krummel's method to detect breakpoints in regression models. RolReg, which is freely available upon request from the author, could useful to detect proper breakpoints in ecological laws

Delayed control of ecological and biological networks

Evolutionary Network Control (ENC) was introduced in 2011 to permit the control of any kind of ecological and biological networks, with an arbitrary number of nodes and links. To date, ENC has been applied with the idea to control biological and ecological networks since the beginning of their system dynamics. This approach has shown to be effective in the control of both continuous-time and discrete-time networks. However a delayed control, where network dynamics are controlled only from a certain point on, could be more economic from a computational viewpoint, and also more feasible from an applicative perspective. For this reason, ENC is further upgraded here to realize the delayed control of ecological and biological nets

WORTHY: a new model for ecological ranking and evaluation

Ecological ranking and environmental decision making require that a set of objects (e.g., competing sites for species introduction, or alternative sites for the allocation of man-made features) are listed from the best to the worst one. The resulting ranking is then used to choose which actions to implement; worse and intermediate solutions are immediately excluded, while optimal and sub-optimal solutions are taken into account, discussed and then applied. In this paper, WORTHY is presented as a new model for ecological ranking and evaluation of competing alternatives based on a set of weighted criteria. I have developed WORTHY model with the goal of employing a TOPSIS-like algorithm for worthy solutions in situations of environmental and ecological conflict management. Compared to TOPSIS algorithm, WORTHY allows to: a) decide the type of normalization, b) build an user-defined decision function, c) perform what-if analysis and d) sensitivity analysis

Where do diaspores come from? Reverse wind modelling unveils plant colonization trajectories

Abstract In alpine habitats, wind is the predominant dispersal vector of diaspores (seeds and spores). The wind flow field in mountain areas depends on the interaction of wind with topography which creates very complex patterns for both wind directions and speeds. Most alpine species utilize wind transport for diaspore dispersal, and more than 90% are anemochorous. The transport of diaspores is to date considered a forward (ahead in time) problem, i.e. from actual diaspore locations to future ones. I argue here that, using appropriate reverse mathematical modelling, the problem can be reversed: starting from actual locations of plants and diaspores, one can evince the trajectories that led to actual positions. So doing, one can reconstruct the trajectories followed by plant species to reach actual niches. A particular application of this approach is the individuation of corridors followed by exotic plant species. The ad-hoc software Wind-Lab has been realized which incorporates both forward and backward wind modelling. The model described here might be of importance in geobotany, climatic ecology and plant conservation biology