Operational and technological developments in maritime warfare: implications for the Western Pacific

There is a growing emphasis on maritime capabilities in the development of military forces in the Western Pacific region, particularly in Northeast and Southeast Asia. This reflects both the relative economic prosperity of the region and a growing concern over maritime security issues. Regional countries are seeking to take advantage of the technological developments which have occurred in recent decades in the field of maritime warfare; some are taking steps towards defining their force structures in terms of what can be built locally and what benefits can be gained for their economic development as a whole from transfers of technology. For all regional navies new issues have arisen. There is the thorny question of balancing resources as well as the risk of opting for too high a military capability and being left with the wrong weapon in the wrong fight. Much of the new equipment entering regional force structures is based on state-of-the-art technology and it is necessary to develop the ability both to operate and to maintain it. New problems are faced: of training and management, of shore-side support, and of testing and evaluation. Technologies which lead to force structuring to suit the unique environment of the region also lead to increased rigour in defining missions, tasks and requirements, and ultimately to the refinement of doctrine and tactics. This monograph is based on papers delivered at a seminar jointly hosted by the Royal Australian Navy's Maritime Studies Program and the Australian Naval Institute at HMAS Watson. It explores recent operational and technological developments In all aspects of maritime warfare - air, surface and sub-surface - and touches on many of the issues facing force planners in respect to the future of maritime security

Approximate Bayesian estimation of coevolutionary arms races.

Exaggerated traits involved in species interactions have long captivated the imagination of evolutionary biologists and inspired the durable metaphor of the coevolutionary arms race. Despite decades of research, however, we have only a handful of examples where reciprocal coevolutionary change has been rigorously established as the cause of trait exaggeration. Support for a coevolutionary mechanism remains elusive because we lack generally applicable tools for quantifying the intensity of coevolutionary selection. Here we develop an approximate Bayesian computation (ABC) approach for estimating the intensity of coevolutionary selection using population mean phenotypes of traits mediating interspecific interactions. Our approach relaxes important assumptions of a previous maximum likelihood approach by allowing gene flow among populations, variable abiotic environments, and strong coevolutionary selection. Using simulated data, we show that our ABC method accurately infers the strength of coevolutionary selection if reliable estimates are available for key background parameters and ten or more populations are sampled. Applying our approach to the putative arms race between the plant Camellia japonica and its seed predatory weevil, Curculio camelliae, provides support for a coevolutionary hypothesis but fails to preclude the possibility of unilateral evolution. Comparing independently estimated selection gradients acting on Camellia pericarp thickness with values simulated by our model reveals a correlation between predicted and observed selection gradients of 0.941. The strong agreement between predicted and observed selection gradients validates our method

Coevolution slows the disassembly of mutualistic networks

Important groups of mutualistic species are threatened worldwide, and identifying factors that make them more or less fragile in the face of disturbance is becoming increasingly critical. Although much research has focused on identifying the ecological factors that favor the stability of communities rich in mutualists, much less has been devoted to understanding the role played by historical and contemporary evolution. Here we develop mathematical models and computer simulations of coevolving mutualistic communities that allow us to explore the importance of coevolution in stabilizing communities against anthropogenic disturbance. Our results demonstrate that communities with a long history of coevolution are substantially more robust to disturbance, losing individual species and interactions at lower rates. In addition, our results identify a novel phenomenon—coevolu-tionary rescue—that mitigates the impacts of ongoing anthropogenic disturbance by rewiring the network structure of the community in a way that compensates for the extinction of individual species and interactions.Fil: Nuismer, Scott L.. University of Idaho; Estados UnidosFil: Week, Bob. University of Idaho; Estados UnidosFil: Aizen, Marcelo Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche. Laboratorio de Ecotono; Argentin

A unified model of species abundance, genetic diversity, and functional diversity reveals the mechanisms structuring ecological communities

International audienceBiodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Within ecological communities drift, dispersal, speciation, and selection operate simultaneously to shape patterns of biodiversity. Reconciling the relative importance of these is hindered by current models and inference methods, which tend to focus on a subset of processes and their resulting predictions. Here we introduce Massive Eco-evolutionary Synthesis Simulations (MESS), a unified mechanistic model of community assembly, rooted in classic island biogeography theory, which makes temporally explicit joint predictions across three biodiversity data axes: i) species richness and abundances; ii) population genetic diversities; and iii) trait variation in a phylogenetic context. Using simulations we demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. MESS is unique in generating predictions of community-scale genetic diversity, and in characterizing joint patterns of genetic diversity, abundance, and trait values. MESS unlocks the full potential for investigation of biodiversity processes using multi-dimensional community data including a genetic component, such as might be produced by contemporary eDNA or metabarcoding studies. We combine MESS with supervised machine learning to fit the parameters of the model to real data and infer processes underlying how biodiversity accumulates, using communities of tropical trees, arthropods, and gastropods as case studies that span a range of data availability scenarios, and spatial and taxonomic scales