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Munch, SB, Giron-Nava A, Sugihara G.  2018.  Nonlinear dynamics and noise in fisheries recruitment: A global meta-analysis. Fish and Fisheries. 19:964-973.   10.1111/faf.12304   AbstractWebsite

The relative importance of environmental and intrinsic controls on recruitment in fishes has been studied for over a century. Despite this, we are not much closer to predicting recruitment. Rather, recent analyses suggest that recruitment is virtually independent of stock size and, instead, seems to occur in distinct environmental regimes. This issue of whether or not recruitment and subsequent production are coupled to stock size is highly relevant to management. Here, we apply empirical dynamical modelling (EDM) to a global database of 185 fish populations to address the questions of whether or not variation in recruitment is (a) predictable and (b) coupled to stock size. We find that a substantial fraction of recruitment variation is predictable using only the observed history of fluctuations (similar to 40% on average). In addition, although recruitment is often coupled to stock size (107 of 185 stocks), stock size alone explains very little of the variation in recruitment; In similar to 90% of the stocks analysed, EDM forecasts have substantially lower prediction error than models based solely on stock size. We find that predictability varies across taxa and improves with the number of generations that have been sampled. In the light of these results, we suggest that EDM will be of greatest use in managing relatively short-lived species.

Ushio, M, Hsieh CH, Masuda R, Deyle ER, Ye H, Chang CW, Sugihara G, Kondoh M.  2018.  Fluctuating interaction network and time-varying stability of a natural fish community. Nature. 554:360-+.   10.1038/nature25504   AbstractWebsite

Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time(1-3). Although this theory has experimental support(2,4,5),evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time)6 and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series(6-9) and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.

Liu, H, Fogarty MJ, Hare JA, Hsieh CH, Glaser SM, Ye H, Deyle E, Sugihara G.  2014.  Modeling dynamic interactions and coherence between marine zooplankton and fishes linked to environmental variability. Journal of Marine Systems. 131:120-129.   10.1016/j.jmarsys.2013.12.003   AbstractWebsite

The dynamics of marine fishes are closely related to lower trophic levels and the environment. Quantitatively understanding ecosystem dynamics linking environmental variability and prey resources to exploited fishes is crucial for ecosystem-based management of marine living resources. However, standard statistical models typically grounded in the concept of linear system may fail to capture the complexity of ecological processes. We have attempted to model ecosystem dynamics using a flexible, nonparametric class of nonlinear forecasting models. We analyzed annual time series of four environmental indices, 22 marine copepod taxa, and four ecologically and commercially important fish species during 1977 to 2009 on Georges Bank, a highly productive and intensively studied area of the northeast U.S. continental shelf ecosystem. We examined the underlying dynamic features of environmental indices and copepods, quantified the dynamic interactions and coherence with fishes, and explored the potential control mechanisms of ecosystem dynamics from a nonlinear perspective. We found: (I) the dynamics of marine copepods and environmental indices exhibiting clear nonlinearity; (2) little evidence of complex dynamics across taxonomic levels of copepods; (3) strong dynamic interactions and coherence between copepods and fishes; and (4) the bottom-up forcing of fishes and top-down control of copepods coexisting as target trophic levels vary. These findings highlight the nonlinear interactions among ecosystem components and the importance of marine zooplankton to fish populations which point to two forcing mechanisms likely interactively regulating the ecosystem dynamics on Georges Bank under a changing environment. (C) 2013 Elsevier B.V. All rights reserved.

Perretti, CT, Munch SB, Sugihara G.  2013.  Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data. Proceedings of the National Academy of Sciences of the United States of America. 110:5253-5257.   10.1073/pnas.1216076110   AbstractWebsite

Accurate predictions of species abundance remain one of the most vexing challenges in ecology. This observation is perhaps unsurprising, because population dynamics are often strongly forced and highly nonlinear. Recently, however, numerous statistical techniques have been proposed for fitting highly parameterized mechanistic models to complex time series, potentially providing the machinery necessary for generating useful predictions. Alternatively, there is a wide variety of comparatively simple model-free forecasting methods that could be used to predict abundance. Here wepose a rather conservative challenge and ask whether a correctly specified mechanistic model, fit with commonly used statistical techniques, can provide better forecasts than simple model-free methods for ecological systems with noisy nonlinear dynamics. Using four different control models and seven experimental time series of flour beetles, we found that Markov chain Monte Carlo procedures for fitting mechanistic models often converged on best-fit parameterizations far different from the known parameters. As a result, the correctly specified models provided inaccurate forecasts and incorrect inferences. In contrast, a model-free method based on state-space reconstruction gave the most accurate short-term forecasts, even while using only a single time series from the multivariate system. Considering the recent push for ecosystem-based management and the increasing call for ecological predictions, our results suggest that a flexible model-free approach may be the most promising way forward.

Courchamp, F, Sugihara G.  1999.  Modeling the biological control of an alien predator to protect island species from extinction. Ecological Applications. 9:112-123.   10.2307/2641172   AbstractWebsite

Introduced feral cat (Felis catus) populations are an important threat to many island vertebrate populations and to bird species in particular. Elimination of feral cat populations is desirable in most of these ecosystems. Release of a parasite species in these mostly immune-naive populations is thought to be an efficient eradication measure. Such an approach is theoretically investigated here, using a mathematical model that describes the effects of introducing a virus into the cat population on population dynamics of both the cat and its prey. We studied the effects of two types of introduced feline viruses: Feline Immunodeficiency Virus and Feline Leukemia Virus, both of which are good candidates for eradicating a cat population. Results show that eradication is possible with Feline Leukemia Virus, if natural immunity is sufficiently low. Feline Immunodeficiency Virus cannot fully eradicate cat populations, but can be an effective agent for long-term control of cat populations on islands where total cat eradication is not possible (e.g., there is a high likelihood of continued introduction of cats) or not desirable (e.g., when rats are present). Culling, which by itself would require a very prolonged and logistically demanding effort to eliminate cat populations, may be more efficient when applied simultaneously with virus introduction.