Summary Habitat connectivity is a major concern in biodiversity conservation. Network analysis provides efficient tools for assessing landscape connectivity and identifying priority areas for protection. Widespread approaches consist of ranking individual habitat patches by their importance for connectivity. However, depending on the spatial arrangement of patches, and on the complementarity or redundancies between different patches in the network, the group of patches that together best contribute to connectivity may differ from the top individual patches. Here we apply individual ranking (single‐node) and group prioritization (multi‐node) methods to identify key patches in the habitat network of 20 bird species in Catalonia, Spain. We compare single‐node evaluation using the landscape index probability of connectivity (PC) and two multi‐node evaluations that focus on two different aspects of connectivity: reachability and fragmentation. We quantify how well the species’ habitats and key patches are covered by the Natura 2000 network of protected areas, as given by all three methods. We discuss some species‐specific differences between results, and general, multi‐species solutions. The key patches for reachability were widely scattered, while patches that best prevented fragmentation were concentrated in core areas. Key patches according to single‐node PC were in intermediate position, although more similar to the fragmentation patches. The patches that minimized fragmentation were highlighted as more crucial for low‐mobility species, while reachability patches scored higher for long‐distance dispersers. For most species, the key patches were not better protected than total habitat. We identify two main areas that concentrate priority patches for all the studied species (including the endangered Neophron percnopterus) and offer suggestions for the potential improvement of the Natura 2000 network. Our work provides a significant step towards the inclusion of multi‐node prioritization in landscape network analysis. The concept of multi‐node reachability, especially, provides an additional viewpoint to the assessment of connectivity. The multi‐patch algorithm we applied optimizes efficiency, overcoming computational limits associated with the high number of combinations that potentially arise in multi‐node analysis. We believe that a combination of multi‐node evaluations and PC has the potential to increase the realism and applicability of landscape network analysis for biodiversity conservation.
Network models are among the most powerful tools in systems ecology. Since trophic relationships (i.e. who eats whom) are among the most frequent interspecific interactions, food webs serve well as system models. In order to better understand ecosystem dynamics, neither strictly local (focusing on individual species) nor strictly global (focusing on the whole ecosystem) approaches are adequate. This mesoscale view on network links suggests to quantify indirect interactions up to some reasonable range and a mesoscale view on network nodes suggests to identify a small set of nodes that are in the most important network positions. We present some examples taking this mesoscale view in ecosystem modelling and use these to discuss the mesoscale perspective. For systems-based conservation management, we suggest to focus on keystone species complexes that are determined considering their indirect interaction neighbourhood. This approach provides a systems-based alternative that hopefully increases to efficiency of future conservation efforts: a small set of system components are targeted in such a way that a large set of the remaining elements are benefited. Challenges Using systems models in ecology has quite a long history [1,2], supporting the view that ecology is essentially the science of coexistence among multiple players. Different kinds of interactions among organisms are the grist for the mill of network modelling: trophic networks describe carbon flows between producers and consumers [3], pollination networks represent inter-specific effects between plants and pollinators [4,5] and co-occurrence networks summarize statistically inferred interactions, typically between microbes [6]. In all of these networks, whatever is the definition of nodes (species, functional groups, OTUs) and links (predation, association), dependencies are represented, being either directional or mutual. If the network is wisely defined, it is a holistic model of a more or less "whole" system. A general strategy of systems approaches in biology is to cross levels of hierarchical organization (i.e. individual, population, community, ecosystem; infraindividual levels not considered in this paper) by integrating pieces of local knowledge and looking for emergent properties [7]. Network analysis offer possibilities to study and quantify part-to-whole relationships: how can smaller components (like species) compose a system (like a lake community) and how can system-level properties (e.g. food web connectance) constrain the behaviour of its components (by various mechanisms including energetics, informational
The selection of reserves for biodiversity conservation involves the evaluation of multiple criteria, ranging from rep� resentati�eness of ecological features to anthropogenic interests and spatial configuration. Among the principal spatial attributes to be considered, connectivity has received particular emphasis in response to the escalating threat of habitat loss and fragmenta� tion. Connectivity is an intrinsic property of networks. Consequently, we have observed the gradual development of the concept of reser�e networks� enlisting also tools from the mathematical branch of network theor�. Here� we first outline three ke� aspects of reserve selection for connectivity conservation based on network analysis. 1� It may be based on the application of topologi� cal indices, which take into consideration only the geographical position of the habitat patches, or area�weighted indices, which add a premium to larger patches. 2� It may be done through single�node analysis, where the relative importance of patches is e�aluated indi�iduall�� or with the more efficient multi�node anal�sis� where we search for the optimal group of patches that best complement each other in the role of maintaining connectivity. 3� The goal of the selection may be to avoid fragmentation of the population into isolated portions, or to ensure that reachability is maintained to all habitat patches, including peripheral sites. In previous studies, we had introduced multi�node analysis to the prioritization of reserves, using fragmentation and reachability indices, but these were limited to topology only. Here, we present an improved approach where multi�node prioritization is per� formed with area�weighted fragmentation. We apply it to 20 bird species in Catalonia, Spain. In comparison with single�node and/or topological fragmentation, we observed here a decentralization of the selected reserve sets: they included not only the main core population, but also secondary clusters of well�connected habitat. This may potentially bring two added advantages to the reser�e network: spreading of risk� and inclusion of a wider �ariet� of local genetic profiles. We propose combining this approach with topological reachability, to account for peripheral populations and maximize accessibility to the entire network. Abbreviations: dPC-node connectivity value based on PC; PC-Probability of Connectivity index.
Important species may be in critically central network positions in ecological interaction networks. Beyond quantifying which one is the most central species in a food web, a multinode approach can identify the key sets of the most central n species as well. However, for sets of different size n, these structural keystone species complexes may differ in their composition. If larger sets contain smaller sets, higher nestedness may be a proxy for predictive ecology and efficient management of ecosystems. On the contrary, lower nestedness makes the identification of keystones more complicated. Our question here is how the topology of a network can influence nestedness as an architectural constraint. Here, we study the role of keystone species complexes in 27 real food webs and quantify their nestedness. After quantifying their topology properties, we determine their keystone species complexes, calculate their nestedness, and statistically analyze the relationship between topological indices and nestedness. A better understanding of the cores of ecosystems is crucial for efficient conservation efforts, and to know which networks will have more nested keystone species complexes would be a great help for prioritizing species that could preserve the ecosystem’s structural integrity.
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