Quantifying the Capacity for Assisted Migration to Achieve Conservation and Forestry Goals Under Climate Change

Aim: Many tree species may be threatened with declines in range and biomass, or even extinction, if they cannot disperse or adapt quickly enough to keep pace with climate change. One potential, and potentially risky, strategy to mitigate this threat is assisted migration (AM), the intentional movement of species to facilitate population range shifts to more climatically suitable locations under climate change. The ability for AM to minimise risk and maximise conservation and forestry outcomes depends on a multi-faceted decision process for determining, what, where and how much to move. We provide an assessment on how the benefits and risks of AM could affect the decision-making process. Location: Mountainous coastal western United States. Taxon: Trees. Methods: We used a dynamic vegetation model parameterised with 23 tree species. Results: We found that most of the modelled species are likely to experience a substantial decline in biomass, with many potentially facing regional extinction by 2100 under the high-emission SSP5-85 climate-change scenario. Though simulations show AM had little effect on the forestry goal of total biomass across all species, its effects on the conservation goal of promoting individual species' persistence were far more substantial. Among eight AM strategies (differing in the life cycle stage of movement and target destination selection criteria), the approach that conserved the highest biomass for individual species involved relocating target seedlings to areas that recently experienced fire. Although this strategy significantly reduced extinction risk for six at-risk species compared with no action, it also slightly reduced biomass of four species, due to increasing competition. Species with relatively weak tolerance to drought, fire or high temperature were the most likely candidate groups for AM. Main Conclusions: Our simulations indicate that AM can aid conservation by reducing extinction risks for species vulnerable to climate change, but it has limited impact on forestry-specific goals, affecting overall biomass minimally. This model framework could be applied to other forest ecosystems to evaluate the efficacy of AM globally

International Business Cycle Spillovers

We apply Diebold-Yilmaz spillover index methodology to monthly industrial production indices to study business cycle interdependence among G-6 industrialized countries since 1958. The business cycle spillover index fluctuates substantially over time, increasing especially after the 1973-75, 1981-82 and 2001 U.S. recessions. The band within which the spillover index fluctuates has widened since the start of the globalization process in the early 1990s. Our most important result, however, concerns the current state of the world economy: In a matter of four months from September to December 2008, the business cycle spillover index recorded the sharpest increase ever, reaching a record level as of December 2008 (See http://data.economicresearchforum.org/erf/bcspill.aspx?lang=en for updates of the spillover plot). Focusing on directional spillover measures, we show that in the current episode the shocks are mostly originating from the United States and spreading to other industrialized countries. We also show that, throughout the period of analysis, the U.S. (1980s and 2000s) and Japan (1970s and 2000s) have been the major transmitters of shocks among the industrialized countries

Acoustic sequences in non-human animals: a tutorial review and prospectus.

Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise - let alone understand - the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near-future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, 'Analysing vocal sequences in animals'. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial-style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality.This review was developed at an investigative workshop, “Analyzing Animal Vocal Communication Sequences” that took place on October 21–23 2013 in Knoxville, Tennessee, sponsored by the National Institute for Mathematical and Biological Synthesis (NIMBioS). NIMBioS is an Institute sponsored by the National Science Foundation, the U.S. Department of Homeland Security, and the U.S. Department of Agriculture through NSF Awards #EF-0832858 and #DBI-1300426, with additional support from The University of Tennessee, Knoxville. In addition to the authors, Vincent Janik participated in the workshop. D.T.B.’s research is currently supported by NSF DEB-1119660. M.A.B.’s research is currently supported by NSF IOS-0842759 and NIH R01DC009582. M.A.R.’s research is supported by ONR N0001411IP20086 and NOPP (ONR/BOEM) N00014-11-1-0697. S.L.DeR.’s research is supported by the U.S. Office of Naval Research. R.F.-i-C.’s research was supported by the grant BASMATI (TIN2011-27479-C04-03) from the Spanish Ministry of Science and Innovation. E.C.G.’s research is currently supported by a National Research Council postdoctoral fellowship. E.E.V.’s research is supported by CONACYT, Mexico, award number I010/214/2012.This is the accepted manuscript. The final version is available at http://dx.doi.org/10.1111/brv.1216

Modeling community dynamics in a fragmented landscape

By reducing habitat connectivity and availability, landscape fragmentation can have a strong effect on biological communities. Since empirical studies in community ecology can require long-term surveys and include large numbers of variables, it is often more efficient to study these systems with predictive mathematical models. Using a dataset from a large-scale study of experimental fragmentation in plant communities as a guide, I created an ordinary differential equation model to describe community dynamics in an array of isolated patches. Both between-patch dispersal and within-patch competition can affect the rate of change in population size of any discrete patch in the array. After developing the mathematical model, I treated it as a linear system (within a small timeframe) and estimated the growth rates, competition coefficients, and influences from source habitats using two species (Solidago canadensis and Cornus drummondii) from an experimental dataset. With these parameters, I calculated a theoretical array of patch communities, which I compared to the empirical fragmentation dataset. Unlike many previous models, mine emphasizes the effects of dispersal range and between-patch distances