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The role of phenotypic plasticity in adaptive evolution has been debated for decades. This is because the strength of natural selection is dependent on the direction and magnitude of phenotypic responses to environmental signals.... more
The role of phenotypic plasticity in adaptive evolution has been debated for decades. This is because the strength of natural selection is dependent on the direction and magnitude of phenotypic responses to environmental signals. Therefore, the connection between plasticity and adaptation will depend on the patterns of plasticity harbored by ancestral populations before a change in the environment. Yet few studies have directly assessed ancestral variation in plasticity and tracked phenotypic changes over time. Here we resurrected historic propagules of Daphnia spanning multiple species and lakes in Wisconsin following the invasion and proliferation of a novel predator (spiny waterflea, Bythotrephes longimanus). This approach revealed extensive genetic variation in predator-induced plasticity in ancestral populations of Daphnia. It is unlikely that the standing patterns of plasticity shielded Daphnia from selection to permit long-term coexistence with a novel predator. Instead, this variation in plasticity provided the raw materials for Bythotrephes-mediated selection to drive rapid shifts in Daphnia behavior and life history. Surprisingly, there was little evidence for the evolution of trait plasticity as genetic variation in plasticity was maintained in the face of a novel predator. Such results provide insight into the link between plasticity and adaptation and highlight the importance of quantifying genetic variation in plasticity when evaluating the drivers of evolutionary change in the wild.
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ABSTRACT Ecosystem dynamics are often complex, nonlinear, and characterized by critical thresholds or phase shifts. To implement sustainable management plans, resource managers need to accurately forecast species abundance. Moreover, an... more
ABSTRACT Ecosystem dynamics are often complex, nonlinear, and characterized by critical thresholds or phase shifts. To implement sustainable management plans, resource managers need to accurately forecast species abundance. Moreover, an ecosystem-based approach to management requires forecasting the dynamics of all relevant species and the ability to anticipate indirect effects of management decisions. It is therefore crucial to determine which forecasting methods are most robust to observational and structural uncertainty. Here we describe a nonparametric method for multispecies forecasting and evaluate its performance relative to a suite of parametric models. We found that, in the presence of noise, it is often possible to obtain more accurate forecasts from the nonparametric method than from the model that was used to generate the data. The inclusion of data from additional species yielded a large improvement for the nonparametric model, a smaller improvement for the control model, and only a slight improvement for the alternative parametric models. These results suggest that flexible nonparametric modeling should be considered for ecosystem management.
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Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but it has had limited... more
Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM), an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a “metabolic time step,” our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19% on average), with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate, rather than the form, of population dynamics, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable, at least approximately, though not for...
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Fluctuations in the population abundances of interacting species are widespread. Such fluctuations could be a response to abiotic factors, biotic interactions, or a combination of the two. Correctly identifying the drivers is critical for... more
Fluctuations in the population abundances of interacting species are widespread. Such fluctuations could be a response to abiotic factors, biotic interactions, or a combination of the two. Correctly identifying the drivers is critical for effective population management. However, such effects are not always static in nature. Nonlinear relationships between abiotic factors and biotic interactions make it difficult to parse true effects. We used a type of nonlinear forecasting, empirical dynamic modeling, to investigate the context‐dependent species interaction between a common fish (three‐spine stickleback) and an endangered one (northern tidewater goby) in a fluctuating environment: a central California bar‐built estuary. We found little evidence for competition, instead both species largely responded independently to abiotic conditions. Stickleback were negatively affected by sandbar breaching. The strongest predictor of tidewater goby abundance was stickleback abundance; however, ...
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Chaotic dynamics appear to be prevalent in short‐lived organisms including plankton and may limit long‐term predictability. However, few studies have explored how dynamical stability varies through time, across space and at different... more
Chaotic dynamics appear to be prevalent in short‐lived organisms including plankton and may limit long‐term predictability. However, few studies have explored how dynamical stability varies through time, across space and at different taxonomic resolutions. Using plankton time series data from 17 lakes and 4 marine sites, we found seasonal patterns of local instability in many species, that short‐term predictability was related to local instability, and that local instability occurred most often in the spring, associated with periods of high growth. Taxonomic aggregates were more stable and more predictable than finer groupings. Across sites, higher latitude locations had higher Lyapunov exponents and greater seasonality in local instability, but only at coarser taxonomic resolution. Overall, these results suggest that prediction accuracy, sensitivity to change and management efficacy may be greater at certain times of year and that prediction will be more feasible for taxonomic aggr...
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Ecological forecasts are potentially of great value for managing fisheries and for stakeholders dependent on their long-term sustainability. Yet, most forecasting approaches are data-intensive, requiring information not just on the focal... more
Ecological forecasts are potentially of great value for managing fisheries and for stakeholders dependent on their long-term sustainability. Yet, most forecasting approaches are data-intensive, requiring information not just on the focal species but also on ecological interactions and the physical environment. Empirical dynamic modeling (EDM) is an equation-free approach to forecasting species’ abundance using only data on past abundance, but the time series required for this approach must be long enough to reconstruct the dynamics of the system. This requirement is rarely met, especially for long-lived species. Here we used simulations and empirical data to demonstrate that incorporating time series from multiple age classes can improve our ability to forecast abundance compared to a single age class or index of total abundance. Including data from multiple age classes produced the greatest gains in forecast accuracy when time series were the shortest. Overall, our results show tha...
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Transgenerational plasticity (TGP) occurs when phenotypes are shaped by the environment in both the current and preceding generations. Transgenerational responses to rainfall, CO 2 and temperature suggest that TGP may play an important... more
Transgenerational plasticity (TGP) occurs when phenotypes are shaped by the environment in both the current and preceding generations. Transgenerational responses to rainfall, CO 2 and temperature suggest that TGP may play an important role in how species cope with climate change. However, little is known about how TGP will evolve as climate change continues. Here, we provide a quantitative test of the hypothesis that the predictability of the environment influences the magnitude of the transgenerational response. To do so, we take advantage of the latitudinal decrease in the predictability of temperatures in near shore waters along the US East Coast. Using sheepshead minnows ( Cyprinodon variegatus ) from South Carolina, Maryland, and Connecticut, we found the first evidence for a latitudinal gradient in thermal TGP. Moreover, the degree of TGP in these populations depends linearly on the decorrelation time for temperature, providing support for the hypothesis that thermal predicta...
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Question: How can one demonstrate that coloration is an honest signal of sexual development? Hypothesis: Coloration in a cyprinid fish is positively related to the development of testes. Species and location: Sheepshead minnows... more
Question: How can one demonstrate that coloration is an honest signal of sexual development? Hypothesis: Coloration in a cyprinid fish is positively related to the development of testes. Species and location: Sheepshead minnows (Cyprinodon variegatus) from South Carolina and Connecticut moved to Santa Cruz, California. Methods: Common garden experiment involving individually grown fish, colour pattern assessment, and the development of male gonads. Results: Males developed bright iridescent blue coloration on their upper parts in front of the dorsal fin through four stages that were positively related to the growth of testes. Such parallel development between sexual coloration and gonad mass was consistent between populations. Conclusions: Coloration in male sheepshead minnows provides an accurate signal of the maturity status of males, which can thus inform both mate choice and intrasexual competition.
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Climate change is rapidly altering the thermal environment in terrestrial and aquatic systems. Transgenerational thermal plasticity (TGP) – which occurs when the temperatures experienced by the parental generation prior to the... more
Climate change is rapidly altering the thermal environment in terrestrial and aquatic systems. Transgenerational thermal plasticity (TGP) – which occurs when the temperatures experienced by the parental generation prior to the fertilization of gametes results in a change in offspring reaction norms – may mitigate the effects of climate change. Although “maternal effects” have been widely studied, relatively little is known about TGP effects in vertebrates, particularly paternal contributions. We used artificial fertilization to cross sheepshead minnow (Cyprinodon variegatus) parents exposed to either low (26°C) or high (32°C) temperatures and measured growth rates of the offspring over the first 8 weeks of life at both low and high temperatures. A linear mixed effects model was employed to quantify the effects of maternal, paternal, and offspring temperatures on offspring growth and fecundity. We found that the offspring growth rate up to 63 days post-hatch was affected by both the ...
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Significance Human activities can alter the behavior of wildlife. Although behavior is known to affect species interactions and demography, behavioral feedbacks are often absent from the types of population models used to understand how... more
Significance Human activities can alter the behavior of wildlife. Although behavior is known to affect species interactions and demography, behavioral feedbacks are often absent from the types of population models used to understand how ecosystems respond to anthropogenic change. We show that incorporating empirically measured fish feeding behavior into dynamical models of a coral reef alters how the ecosystem responds to fishing. Fish behavior can cause ecosystem collapse in response to less fishing but also, unexpectedly, to subtle differences in the pace at which fishing increases. Behavioral mechanisms similar to those included in our model are present in many ecological systems, and our findings suggest these mechanisms could inform both fundamental understanding and management strategies in such systems.
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Declines in animal body sizes are widely reported and likely impact ecological interactions and ecosystem services. For harvested species subject to multiple stressors, limited understanding of the causes and consequences of size declines... more
Declines in animal body sizes are widely reported and likely impact ecological interactions and ecosystem services. For harvested species subject to multiple stressors, limited understanding of the causes and consequences of size declines impedes prediction, prevention, and mitigation. We highlight widespread declines in Pacific salmon size based on 60 years of measurements from 12.5 million fish across Alaska, the last largely pristine North American salmon-producing region. Declines in salmon size, primarily resulting from shifting age structure, are associated with climate and competition at sea. Compared to salmon maturing before 1990, the reduced size of adult salmon after 2010 has potentially resulted in substantial losses to ecosystems and people; for Chinook salmon we estimated average per-fish reductions in egg production (−16%), nutrient transport (−28%), fisheries value (−21%), and meals for rural people (−26%). Downsizing of organisms is a global concern, and current tre...
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Research Interests: Evolutionary Biology, Environmental Science, Time Series, Ecology, Phytoplankton, and 15 moreMedicine, Lakes, Animals, Trophic Cascade, Temperature, Trophic Level, Species interactions, Nutrients, Nutrient, Ecosystem, Seasons, Ecological Applications, Resource Control, Lake Ecosystem, and Temporal Scales
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Studies investigating population structure and mixed-stock composition of fish populations frequently use otolith chemistry as a natural tool for discerning stocks. Current methods for estimating mixed-stock composition, however, assume... more
Studies investigating population structure and mixed-stock composition of fish populations frequently use otolith chemistry as a natural tool for discerning stocks. Current methods for estimating mixed-stock composition, however, assume complete accuracy in the training data, which is often not the case. Here we present a method for estimating mixed-stock composition using multivariate continuous data that accounts for uncertainty in the training data. Application of the method to previously reported data for natal homing in weakfish ( Cynoscion regalis ) and simulations based on these data revealed that for sample sizes greater than about 30, the present method provides results that are quite similar to those of previous methods. An advantage of this Bayesian approach over other methods, however, is the ease with which functionals of the model, such as migration distance and direction, can be calculated. It also provides simple means of visualizing spatial structure in the classifi...
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The systematic substitution of direct observational data with synthesized data derived from models during the stock assessment process has emerged as a low-cost alternative to direct data collection efforts. What is not widely... more
The systematic substitution of direct observational data with synthesized data derived from models during the stock assessment process has emerged as a low-cost alternative to direct data collection efforts. What is not widely appreciated, however, is how the use of such synthesized data can overestimate predictive skill when forecasting recruitment is part of the assessment process. Using a global database of stock assessments, we show that Standard Fisheries Models (SFMs) can successfully predict synthesized data based on presumed stock-recruitment relationships, however, they are generally less skillful at predicting observational data that are either raw or minimally filtered (denoised without using explicit stock-recruitment models). Additionally, we find that an equation-free approach that does not presume a specific stock-recruitment relationship is better than SFMs at predicting synthesized data, and moreover it can also predict observational recruitment data very well. Thus...
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Transgenerational plasticity (TGP) is increasingly recognized as a mechanism by which organisms can respond to environments that change across generations. Although recent empirical and theoretical studies have explored conditions under... more
Transgenerational plasticity (TGP) is increasingly recognized as a mechanism by which organisms can respond to environments that change across generations. Although recent empirical and theoretical studies have explored conditions under which TGP is predicted to evolve, it is still unclear whether the effects of the parental environment will remain beyond the offspring generation. Using a small cyprinodontid fish, we explored multigenerational thermal TGP to address two related questions. First (experiment 1), does the strength of TGP decline or accumulate across multiple generations? Second (experiment 2), how does the experience of a temperature novel to both parents and offspring affect the strength of TGP? In the first experiment, we found a significant interaction between F1 and F2 temperatures and juvenile growth, but no effect of egg diameter. The strength of TGP between F0 and F1 generations was similar in both experiments but declined in subsequent generations. Further, exp...
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Small pelagic fish support some of the largest fisheries globally, yet there is an ongoing debate about the magnitude of the impacts of environmental processes and fishing activities on target species. We use a nonparametric, nonlinear... more
Small pelagic fish support some of the largest fisheries globally, yet there is an ongoing debate about the magnitude of the impacts of environmental processes and fishing activities on target species. We use a nonparametric, nonlinear approach to quantify these effects on the Pacific sardine (Sardinops sagax) in the Gulf of California. We show that the effect of fishing pressure and environmental variability are comparable. Furthermore, when predicting total catches, the best models account for both drivers. By using empirical dynamic programming with average environmental conditions, we calculated optimal policies to ensure long-term sustainable fisheries. The first policy, the equilibrium maximum sustainable yield, suggests that the fishery could sustain an annual catch of ∼2.16 × 105 tonnes. The second policy with dynamic optimal effort, reveals that the effort from 2 to 4 years ago impacts the current maximum sustainable effort. Consecutive years of high effort require a reduct...
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Dataset: Grandparental effectsCyprinodon variegatus offspring growth rate in grandparantal experiments from specimens wild caught in the Atlantic during 2014. For a complete list of measurements, refer to the full dataset description in... more
Dataset: Grandparental effectsCyprinodon variegatus offspring growth rate in grandparantal experiments from specimens wild caught in the Atlantic during 2014. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/709762NSF Division of Ocean Sciences (NSF OCE) OCE-113048
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Predicting the dynamics of harvested species is essential for assessing stock status and establishing index-based management strategies. However, conventional approaches for short-lived species predict dynamics poorly, possibly because... more
Predicting the dynamics of harvested species is essential for assessing stock status and establishing index-based management strategies. However, conventional approaches for short-lived species predict dynamics poorly, possibly because unobserved interactions with other species and abiotic factors are often treated as noise. Alternatively, the empirical dynamic modeling (EDM) approach, which uses the time delays of the observed states to compensate for unobserved interactions, may improve the predictions for short-lived species. We test this idea using time series data of two federally managed, short-lived penaeid shrimp species, whose abundances were surveyed over 30 years (1987–2018) across the US Gulf of Mexico. We show that ( i) abundance dynamics of these annual shrimp stocks are well-predicted by EDM, ( ii) the dynamics are spatially similar across most of the gulf, and ( iii) the stock dynamics are characterized by nonlinear density-dependent interaction and vary with tempera...
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Refer to the Readme file for variable descriptions
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
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Complex nonlinear dynamics are ubiquitous in marine ecology. Empirical dynamic modelling can be used to infer ecosystem dynamics and species interactions while making minimal assumptions. Although there is growing enthusiasm for applying... more
Complex nonlinear dynamics are ubiquitous in marine ecology. Empirical dynamic modelling can be used to infer ecosystem dynamics and species interactions while making minimal assumptions. Although there is growing enthusiasm for applying these methods, the background required to understand them is not typically part of contemporary marine ecology curricula, leading to numerous questions and potential misunderstanding. In this study, we provide a brief overview of empirical dynamic modelling, followed by answers to the ten most frequently asked questions about nonlinear dynamics and nonlinear forecasting.
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Model uncertainty and limited data are fundamental challenges to robust management of human intervention in a natural system. These challenges are acutely highlighted by concerns that many ecological systems may contain tipping points,... more
Model uncertainty and limited data are fundamental challenges to robust management of human intervention in a natural system. These challenges are acutely highlighted by concerns that many ecological systems may contain tipping points, such as Allee population sizes. Before a collapse, we do not know where the tipping points lie, if they exist at all. Hence, we know neither a complete model of the system dynamics nor do we have access to data in some large region of state space where such a tipping point might exist. We illustrate how a Bayesian non-parametric approach using a Gaussian process (GP) prior provides a flexible representation of this inherent uncertainty. We embed GPs in a stochastic dynamic programming framework in order to make robust management predictions with both model uncertainty and limited data. We use simulations to evaluate this approach as compared with the standard approach of using model selection to choose from a set of candidate models. We find that model selection erroneously favours models without tipping points, leading to harvest policies that guarantee extinction. The Gaussian process dynamic programming (GPDP) performs nearly as well as the true model and significantly outperforms standard approaches. We illustrate this using examples of simulated single-species dynamics, where the standard model selection approach should be most effective and find that it still fails to account for uncertainty appropriately and leads to population crashes, while management based on the GPDP does not, as it does not underestimate the uncertainty outside of the observed data
Running title: Intrinsic predictability & forecasting 2 3 Title: The intrinsic predictability of 4 ecological time series and its 5 potential to guide forecasting 6 7 Frank Pennekamp1,§, *, Alison C. Iles2,5,6 §, Joshua Garland3, Georgina... more
Running title: Intrinsic predictability & forecasting 2 3 Title: The intrinsic predictability of 4 ecological time series and its 5 potential to guide forecasting 6 7 Frank Pennekamp1,§, *, Alison C. Iles2,5,6 §, Joshua Garland3, Georgina Brennan4, Ulrich 8 Brose5,6, Ursula Gaedke7, Ute Jacob8, Pavel Kratina9, Blake Matthews10, Stephan Munch11, 12, 9 Mark Novak2, Gian Marco Palamara1,13, Björn C. Rall5,6, Benjamin Rosenbaum5,6, Andrea 10 Tabi1, Colette Ward1, Richard Williams14, Hao Ye15, Owen L. Petchey1 11 12 § F. Pennekamp and A. Iles contributed equally to this work 13
The differences in demographic and life-history processes between organisms living in the same population have important consequences for ecological and evolutionary dynamics. Modern statistical and computational methods allow the... more
The differences in demographic and life-history processes between organisms living in the same population have important consequences for ecological and evolutionary dynamics. Modern statistical and computational methods allow the investigation of individual and shared (among homogeneous groups) determinants of the observed variation in growth. We use an Empirical Bayes approach to estimate individual and shared variation in somatic growth using a von Bertalanffy growth model with random effects. To illustrate the power and generality of the method, we consider two populations of marble trout Salmo marmoratus living in Slovenian streams, where individually tagged fish have been sampled for more than 15 years. We use year-of-birth cohort, population density during the first year of life, and individual random effects as potential predictors of the von Bertalanffy growth function's parameters k (rate of growth) and L∞ (asymptotic size). Our results showed that size ranks were larg...
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Cause or Correlation? Three centuries ago, Bishop Berkeley's 1710 classic “A treatise on the nature of human knowledge,” first spelled out the “correlation vs. causation” dilemma. Sugihara et al. (p. 496 , published online 20... more
Cause or Correlation? Three centuries ago, Bishop Berkeley's 1710 classic “A treatise on the nature of human knowledge,” first spelled out the “correlation vs. causation” dilemma. Sugihara et al. (p. 496 , published online 20 September) present an approach to this conundrum, and extend current discussions about causation to dynamic systems with weak to moderate coupling (such as ecosystems). The resulting method, convergent cross mapping can detect causal linkages between time series.
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For many marine species and habitats, climate change and overfishing present a double threat. To manage marine resources effectively, it is necessary to adapt management to changes in the physical environment. Simple relationships between... more
For many marine species and habitats, climate change and overfishing present a double threat. To manage marine resources effectively, it is necessary to adapt management to changes in the physical environment. Simple relationships between environmental conditions and fish abundance have long been used in both fisheries and fishery management. In many cases, however, physical, biological, and human variables feed back on each other. For these systems, associations between variables can change as the system evolves in time. This can obscure relationships between population dynamics and environmental variability, undermining our ability to forecast changes in populations tied to physical processes. Here we present a methodology for identifying physical forcing variables based on nonlinear forecasting and show how the method provides a predictive understanding of the influence of physical forcing on Pacific sardine.
Research Interests: Environmental Science, Climate Change, Fisheries, Nonlinear dynamics, Population Dynamics, and 13 moreMedicine, Multidisciplinary, Time series analysis, Multivariate Analysis, Animals, Fishes, Fishery Stock Assessment and Management, SARDINE, Ecosystem, Time Factors, Pacific ocean, Conservation of Natural Resources, and Environment
Complex nonlinear dynamics are ubiquitous in many fields. Moreover, we rarely have access to all of the relevant state variables governing the dynamics. Delay embedding allows us, in principle, to account for unobserved state variables.... more
Complex nonlinear dynamics are ubiquitous in many fields. Moreover, we rarely have access to all of the relevant state variables governing the dynamics. Delay embedding allows us, in principle, to account for unobserved state variables. Here we provide an algebraic approach to delay embedding that permits explicit approximation of error. We also provide the asymptotic dependence of the first order approximation error on the system size. More importantly, this formulation of delay embedding can be directly implemented using a Recurrent Neural Network (RNN). This observation expands the interpretability of both delay embedding and RNN and facilitates principled incorporation of structure and other constraints into these approaches.
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This archive includes the scripts and data used to run and analyze the simulations involved in the manuscript, <i>"Interaction network structure and spatial patterns influence invasiveness and invasibility in a stochastic model... more
This archive includes the scripts and data used to run and analyze the simulations involved in the manuscript, <i>"Interaction network structure and spatial patterns influence invasiveness and invasibility in a stochastic model of plant communities"</i> by Nicole L. Kinlock and Stephan B. Munch.<br><b>Please see the README file for a detailed guide.</b><br>
Complex nonlinear dynamics are ubiquitous in many fields. Moreover, we rarely have access to all of the relevant state variables governing the dynamics. Delay embedding allows us, in principle, to account for unobserved state variables.... more
Complex nonlinear dynamics are ubiquitous in many fields. Moreover, we rarely have access to all of the relevant state variables governing the dynamics. Delay embedding allows us, in principle, to account for unobserved state variables. Here we provide an algebraic approach to delay embedding that permits explicit approximation of error. We also provide the asymptotic dependence of the first order approximation error on the system size. More importantly, this formulation of delay embedding can be directly implemented using a Recurrent Neural Network (RNN). This observation expands the interpretability of both delay embedding and RNN and facilitates principled incorporation of structure and other constraints into these approaches.
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We develop a model for Bayesian selection in high order Markov chains through an extension of the mixture transition distribution of Raftery (1985). We demonstrate two uses for the model: parsimonious approximation of high order dynamics... more
We develop a model for Bayesian selection in high order Markov chains through an extension of the mixture transition distribution of Raftery (1985). We demonstrate two uses for the model: parsimonious approximation of high order dynamics by mixing lower order transition models, and model selection through over-specification and shrinkage via priors for sparse probability vectors. We discuss properties of the model and demonstrate its utility with simulation studies. We further apply the model to a data analysis from the high-order Markov chain literature and a novel application to pink salmon abundance time series.
Chaotic dynamics are thought to be rare in natural populations, but this may be due to methodological and data limitations, rather than the inherent stability of ecosystems. Following extensive simulation testing, we applied multiple... more
Chaotic dynamics are thought to be rare in natural populations, but this may be due to methodological and data limitations, rather than the inherent stability of ecosystems. Following extensive simulation testing, we applied multiple chaos detection methods to a global database of 175 population time series and found evidence for chaos in >30%. In contrast, fitting traditional one-dimensional models identified <10% as chaotic. Chaos was most prevalent among plankton and insects and least among birds and mammals. Lyapunov exponents declined with generation time and scaled as the -1/6 power of mass among chaotic populations. These results demonstrate that chaos is not rare in natural populations, indicating that there may be intrinsic limits to ecological forecasting and cautioning against the use of steady-state approaches to conservation and management.