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One of the central problems in ecology is how to scale from small‐scale observations and experiments to large‐scale patterns and processes. One approach to such upscaling is to use dynamic simulation models, but their application to large scales relevant for management is limited by computational costs, and their outputs are difficult to analyse without a systematic strategy. Our general objective is to propose such a strategy. The idea is to approximate the dynamics of detailed simulation models through a set of states, external drivers, and transition matrices, and then use Markov chain and network analysis of the resulting transition matrices to gain insights into the dynamics of the underlying detailed model. We used the individual‐based model COIRON, which simulates the dynamics of semiarid grass steppes in Patagonia (Argentina) under alternative grazing management, as example. Our specific objectives are to identify pathways of degradation and rehabilitation, as well as critical grazing thresholds and early‐warning vegetation states to guide sustainable grazing management in these steppes. Our results indicate nonlinear effects of stocking rate and grazing season on steppe dynamics. Markov chain analysis suggests benefits of seasonal over continuous grazing at intermediate stocking rates, and network analysis of recovery and degradation trajectories shows that intermediate stocking rates maximize differences between grazing seasons. Finally, our analysis identified specific vegetation states as early warning signals that indicate a high risk of irreversible vegetation changes. Patagonian grass steppes should ideally be managed with multi‐paddock grazing at moderate stocking rates around 0.5 sheep ha−1. The transition matrices summarize the relevant key features of the detailed model for larger scales, and applying Markov and network theory provides a systematic strategy to analyse its dynamics to respond to biological questions, both are often difficult to obtain by direct analysis of the detailed model. [ABSTRACT FROM AUTHOR]

1. The problem of scaling up from tractable, small-scale observations and experiments to prediction of largescale patterns is at the core of ecological theory and application, and one of the central problems in ecology. 2. We present and test a general nonparametric framework to upscale spatially explicit and stochastic simulation models. The idea is to design a state space, defined by the important state variables of the small-scale model, and to divide it into a finite number of discrete states. Transition probabilities are then tallied bymonitoring extensive simulation runs of the small-scale model, covering the entire range of initial conditions, states and external drivers that may occur for the desired application. We exemplify our approach by upscaling an individual-based model that simulates the spatiotemporal dynamics of Festuca pallescens steppes under sheep grazing in Western Patagonia, Argentina, with a spatial resolution of 0.3 m × 0.3 m and a 0.15-ha extent. The upscaledmodel simulates a 2500-ha paddock with 0.15-ha resolution and is enriched with additional rules that describe heterogeneity in the local stocking rate at the paddock scale. 3. We obtained 24 transition matrices that governed the upscaled model for different combinations of stocking rates and annual precipitation. The upscaled model produced excellent predictions for the long-term dynamics, but as expected, it did not fully capture the interannual dynamics of the original model. Rules for heterogeneity in the local stocking rate allowed for emergence of realistic vegetation patterns as commonly observed for water points in arid rangelands. 4. Our general nonparametric upscaling approach can be applied to a wide range of stochastic simulation models in which the dynamics can be approximated by a set of states, transitions and external drivers. Because estimation of the transition probabilities can be done parallel, our approach can be applied to a wide range ofmodels of intermediate complexity.Our approach closes a gap in our ability to scale up fromsmall scales, where the biological knowledge is available, to larger scales that are relevant for management. [ABSTRACT FROM AUTHOR]

In this work we analyse the pollination community in a South American forest known as ‘talar’. This is a vegetal woody community that inhabits fossil coastal banks characterized by seasonal temperate weather and calcareous soil, at the coast of the Río de la Plata, in the province of Buenos Aires, Argentina. We obtained data of the interactions between anthophylous insects and entomophylous flowering plants over an extensive period of time. We showed that pollination system parameters, such as partners’ identity, system size, and connectance, fluctuated among months, when sampled year-long. Maximal network size occurred in early spring and early autumn, when both the number of mutualistic species and the number of interactions peaked, and this was also when network asymmetry was higher than average. Monthly connectance of the plant-flower visitor matrix decreased to its lowest values at these peaks. Available data suggest that cumulative traditional connectance (i.e. the connectance calculated as the whole number of interactions registered in the community divided by the full size system) underestimates actual connectance values by a factor of c. 3 ×. Monthly values of connectance decreased exponentially as system size increased, and the distribution of interactions per species followed power-law regimes for animals, and truncated power-law regimes for plants, in accordance with patterns previously deduced from among-network cumulative communities studies. We think that either within or and among pollination networks, systems that are organized as power-law regimes may be a basic property of these webs, and provide examples of the fact. Both seasonal changes and interactions between mutualists like competition, and some degree of facilitation, may be very important to understand the performance of the system as a whole, and the role and importance of different species in the community. We suggest that communities of plant – pollinators that exhibit extended activity, such as temperate or tropical seasonal ones, should be studied through consecutive plant-pollinator webs rather than cumulative ones. The partition of the system into smaller serial parts allows us to obtain outstanding information of every short period. This information is flattened by the average effect when we considered the combined analysis of the whole data. [ABSTRACT FROM AUTHOR]