8In this paper we introduce a method for determining local interaction rules 9 in animal swarms. The method is based on the assumption that the behavior 10 of individuals in a swarm can be treated as a set of mechanistic rules. 11The principal idea behind the technique is to vary parameters that define a 12 set of hypothetical interactions to minimize the deviation between the forces 13 estimated from observed animal trajectories and the forces resulting from the 14 assumed rule set. We demonstrate the method by reconstructing the interac-15 tion rules from the trajectories produced by a computer simulation. 16 Key words: swarming, behavioral rules, reverse engineering, force match-17 ing. 18 1 The collective motion of living organisms, as manifested by flocking birds, 19 schooling fish, or swarming insects, presents a captivating phenomenon believed 20 to emerge mainly from local interactions between individual group members. In 21 part, the study of swarming and flocking aims to understand how animals use vi-22 sual, audial and other cues to orient themselves with respect to the swarm of which 23 they are part, and how the properties of the swarm as a whole depend on the be-24 haviors of the individual animals. Also when addressing evolutionary questions of 25 behaviour in swarms and flocks, such as the selective advantage of being bold or 26 shy in response to a predator, it is important to understand how the individuals be-27 have based on the relation to their neighbours in the swarm or flock. For example, 28if the question is "If the peripheral of the flock is more exposed to predators, do 29 some individuals cheat the others by staying at the center of the flock where they 30 are more protected?", knowing the effective rules would make it easier to address 31 questions regarding the evolutionary stability of the altruistic behavior. 32Because flocks cannot be understood by studying individuals in isolation, and 33 are difficult to conduct controlled experiments on, understanding the behavioural 34 patterns underlying flocking and swarming is especially challenging. Consequently, 35 collective behavior has been extensively modeled particularly using the agent-based 36 modeling framework, where simple mechanistic behavioral rules are used to gener-37 ate qualitatively realistic swarming behavior, (e.g Aoki, 1982; Reynolds, 1987; Vic-38 sek et al. Yates et al., 2009). The rules usually comprise three kinds of forces: A short-range 42 2 force to avoid collisions with obstacles or other animals; a force adjusting the veloc-43 ity to fit nearby individuals' velocities; and a force for avoiding being alone, e.g. by 44 moving towards the average position of the nearby individuals. However, see e.g. 45 Romanczuk et al. (2009) for an alternative formulation. In addition, drag forces 46 and noise are used to model the medium through which the individuals move, and 47 external forces can be used to model interactions with terrain or predators. 48 The main strength of the agent-based modeling framewor...

Current methods in conservation planning for promoting the persistence of biodiversity typically focus on either representing species geographic distributions or maintaining connectivity between reserves, but rarely both, and take a focal species, rather than a multispecies, approach. Here, we link prioritization methods with population models to explore the impact of integrating both representation and connectivity into conservation planning for species persistence. Using data on 288 Mediterranean fish species with varying conservation requirements, we show that: (1) considering both representation and connectivity objectives provides the best strategy for enhanced biodiversity persistence and (2) connectivity objectives were fundamental to enhancing persistence of small‐ranged species, which are most in need of conservation, while the representation objective benefited only wide‐ranging species. Our approach provides a more comprehensive appraisal of planning applications than approaches focusing on either representation or connectivity, and will hopefully contribute to build more effective reserve networks for the persistence of biodiversity.

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Maintaining and enabling evolutionary processes within meta‐populations are critical to resistance, resilience and adaptive potential. Knowledge about which populations act as sources or sinks, and the direction of gene flow, can help to focus conservation efforts more effectively and forecast how populations might respond to future anthropogenic and environmental pressures. As a foundation species and habitat provider, Zostera marina (eelgrass) is of critical importance to ecosystem functions including fisheries. Here, we estimate connectivity of Z. marina in the Skagerrak–Kattegat region of the North Sea based on genetic and biophysical modelling. Genetic diversity, population structure and migration were analysed at 23 locations using 20 microsatellite loci and a suite of analytical approaches. Oceanographic connectivity was analysed using Lagrangian dispersal simulations based on contemporary and historical distribution data dating back to the late 19th century. Population clusters, barriers and networks of connectivity were found to be very similar based on either genetic or oceanographic analyses. A single‐generation model of dispersal was not realistic, whereas multigeneration models that integrate stepping‐stone dispersal and extant and historic distribution data were able to capture and model genetic connectivity patterns well. Passive rafting of flowering shoots along oceanographic currents is the main driver of gene flow at this spatial–temporal scale, and extant genetic connectivity strongly reflects the “ghost of dispersal past“ sensu Benzie, 1999. The identification of distinct clusters, connectivity hotspots and areas where connectivity has become limited over the last century is critical information for spatial management, conservation and restoration of eelgrass.

We present a direct method for solving the inverse problem of designing isotropic potentials that cause self-assembly into target lattices. Each potential is constructed by matching its energy spectrum to the reciprocal representation of the lattice to guarantee that the desired structure is a ground state. We use the method to self-assemble complex lattices not previously achieved with isotropic potentials, such as a snub square tiling and the kagome lattice. The latter is especially interesting because it provides the crucial geometric frustration in several proposed spin liquids.

Dispersal on the landscape/seascape scale may lead to complex spatial population structure with non‐synchronous demography and genetic divergence. In this study we present a novel approach to identify subpopulations and dispersal barriers based on estimates of dispersal probabilities on the landscape scale. A theoretical framework is presented where the landscape connectivity matrix is analyzed for clusters as a signature of partially isolated subpopulations. Identification of subpopulations is formulated as a minimization problem with a tuneable penalty term that makes it possible to generate population subdivisions with varying degree of dispersal restrictions. We show that this approach produces superior results compared to alternative standard methods. We apply this theory to a dataset of modeled dispersal probabilities for a sessile marine invertebrate with free‐swimming larvae in the Baltic Sea. For a range of critical connectivities we produce a hierarchical partitioning into subpopulations spanning dispersal probabilities that are typical for both genetic divergence and demographic independence. The mapping of subpopulations suggests that the Baltic Sea includes a fine‐scale (100–600 km) mosaic of invisible dispersal barriers. An analysis of the present network of marine protected areas reveal that protection is very unevenly distributed among the suggested subpopulations. Our approach can be used to assess the location and strength of dispersal barriers in the landscape, and identify conservation units when extensive genotyping is prohibitively costly to cover necessary spatial and temporal scales, e.g. in spatial management of marine populations.

Protected areas (PAs) are recognized as the flagship tool to offset biodiversity loss on Earth. Spatial conservation planning seeks optimal designs of PAs that meet multiple targets such as biodiversity representation and population persistence. Since connectivity between PAs is a fundamental requirement for population persistence, several methods have been developed to include connectivity into PA design algorithms. Among these, the eigenvalue decomposition of the connectivity matrix allows for identifying clusters of strongly connected sites and selecting the sites contributing the most to population persistence. So far, this method was only suited to optimize an entire network of PAs without considering existing PAs in the new design. However, a more cost‐effective and realistic approach is to optimize the design of an extended network to improve its connectivity and thus population persistence. Here, we develop a flexible algorithm based on eigenvalue decomposition of connectivity matrices to extend existing networks of PAs while optimizing connectivity and population growth rate. We also include a splitting algorithm to improve cluster identification. The new algorithm accounts for the change in connectivity due to the increased biological productivity often observed in existing PAs. We illustrate the potential of our algorithm by proposing an extension of the network of ∼100 Mediterranean marine PAs to reach the targeted 10% surface area protection from the current 1.8%. We identify differences between the clean slate scenario, where all sites are available for protection, irrespective of their current protection status, and the scenario where existing PAs are forced to be included into the optimized solution. By integrating this algorithm to existing multi‐objective and multi‐specific algorithms of PA selection, the demographic effects of connectivity can be explicitly included into conservation planning.

Conservation and management of natural resources and biodiversity need improved criteria to select functional networks of protected areas. The connectivity within networks due to dispersal is rarely considered, partly because it is unclear how connectivity information can be included in the selection of protected areas. We present a novel and general method that applies eigenvalue perturbation theory (EPT) to select optimum networks of protected areas based on connectivity. At low population densities, characteristic of threatened populations, this procedure selects networks that maximize the growth rate of the overall network. This method offers an improved link between connectivity and metapopulation dynamics. Our framework is applied to connectivities estimated for marine larvae and demonstrates that, for open populations, the best strategy is to protect areas acting as both strong donors and recipients of recruits. It should be possible to implement an EPT framework for connectivity analysis into existing holistic tools for design of protected areas.