Quantifying effects of abiotic and biotic drivers on community dynamics with multivariate autoregressive (MAR) models

Hampton, Stephanie E.; Holmes, Elizabeth E.; Scheef, Lindsay P.; Scheuerell, Mark D.; Katz, Stephen L.; Pendleton, Daniel E.; Ward, Eric J.

doi:10.1890/13-0996.1

Cited by 105 publications

(115 citation statements)

References 47 publications

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“…A more readily scalable alternative would be to estimate specific mutual interactions from time series of abundance. Currently available tools like multivariate autoregression and its generalizations are specifically designed for systems with constant, fixed linear interactions and are sometimes 'fit' as dynamic linear models (DLM) to randomly drifting linear interactions [13,14]. But for nonlinear systems, such models are at best an ad hoc approximation without mechanism or the ability to predict.…”

Section: Introductionmentioning

confidence: 99%

Tracking and forecasting ecosystem interactions in real time

et al. 2016

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Evidence shows that species interactions are not constant but change as the ecosystem shifts to new states. Although controlled experiments and model investigations demonstrate how nonlinear interactions can arise in principle, empirical tools to track and predict them in nature are lacking. Here we present a practical method, using available time-series data, to measure and forecast changing interactions in real systems, and identify the underlying mechanisms. The method is illustrated with model data from a marine mesocosm experiment and limnologic field data from Sparkling Lake, WI, USA. From simple to complex, these examples demonstrate the feasibility of quantifying, predicting and understanding state-dependent, nonlinear interactions as they occur in situ and in real time-a requirement for managing resources in a nonlinear, non-equilibrium world.

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Section: Introductionmentioning

confidence: 99%

Tracking and forecasting ecosystem interactions in real time

et al. 2016

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“…Multivariate autoregressive (MAR) models are a useful tool for estimating the strengths of interspecific interactions and extrinsic forcing responsible for changes in ecological communities [6,9]. MAR models are based on the classic deterministic Gompertz model of population growth, which describes change in abundance through time as a function of intrinsic growth and density dependence.…”

Section: Introductionmentioning

confidence: 99%

Effects of climate change on zooplankton community interactions in an Alaskan lake

Carter

Schindler

Francis

2017

Clim Chang Responses

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Background: Ecological communities are organized by interactions among the biota, and between the biota and external environmental drivers that affect the dynamics of individual taxa. Climate change may alter communities in unexpected ways when environmental drivers have complex effects on individual species that are then transmitted indirectly to other species via biotic interactions. Methods: We used a multivariate autoregressive (MAR) modeling framework to assess the strengths of intrinsic interactions and extrinsic (environmental) forcing responsible for changes in the zooplankton community of a sockeye salmon nursery lake in southwestern Alaska from 1963 to 2009. During this time period there has been a strong trend towards earlier spring ice breakup dates and warmer summer water temperatures. Results: MAR analyses of community time-series showed that water temperature was the dominant driver of change in the zooplankton community; competitive interactions were relatively rare, and only copepods (both cyclopoids and calanoids) were affected by predation (juvenile sockeye salmon). Best-fit community models were used to develop scenarios of zooplankton community composition under several different potential climate conditions and salmon densities and revealed the potential for a shift in the dominant zooplankton taxa in this lake, driven largely by taxon-specific sensitivity to climate and sockeye salmon predation. Conclusions: Simulations suggest that cladocerans will become more prevalent in this community and that calanoid copepods will suffer from ongoing climate warming. These results have important implications for fish in these northern lakes, as they suggest that the production of planktivorous fish should increase with ongoing climate change.

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“…Deyle et al 2016;Perretti et al 2013;Sugihara et al 2012;Ye et al 2015), but whether or not such models provide better forecast than simple linear autoregressive models is an open question (Ward et al 2014). Linear models, like multivariate autoregressive models Hampton et al 2013), can easily be fitted to time series data, and have successfully predicted fish dynamics in some marine ecosystems (Lindegren et al 2009;Lindegren et al 2014;Bell et al 2014). Importantly, this method can also include the effect of extrinsic environmental variables, which for sure is very important in almost any ecological system.…”

Section: Time Series Approachesmentioning

confidence: 99%

“…MAR-models are neatly introduced by Ives et al (2003), and have been used extensively to infer species interactions from time series of different organisms (e.g. Ives et al 2003;Mutshinda et al 2009;Hampton et al 2013;Lindegren et al 2009). The multivariate autoregressive modeling framework is based on discrete Gompertz models:…”

Section: Multivariate Autoregressive Modelsmentioning

confidence: 99%

Functional Extinctions of Species in Ecological Networks

Säterberg¹

2016

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Current rates of extinctions are estimated to be around 1000 times higher than background rates that would occur without anthropogenic impacts. These extinction rates refer to the traditional view of extinctions, i.e. numerical extinctions. This thesis is about another type of extinctions: functional extinctions. Those occur when the abundance of a species is too small to uphold the species’ ecologically interactive role. I have taken a theoretical approach and used dynamical models to investigate functional extinctions and threshold values for species’ mortality rates in ecological networks. More specifically, I have derived threshold values for focal species mortality rates at which another species or the focal species itself goes numerically extinct (Paper I-II), or transgresses some predefined threshold abundance (Paper III). If an increased mortality rate of a focal species causes another species to go numerically extinct, the focal species can be regarded as functionally extinct, since its abundance is no longer large enough to uphold its ecologically interactive role. Such functional extinctions are investigated in the first papers (Paper I-II). In the following paper, limits for both increased and decreased mortality rates of species are explored (Paper III). Paper III also extends the basic theoretical idea developed in paper I-II into a more applied setting. In this paper I develop a time series approach aimed at estimating fishing mortalities associated with a low risk that any species in a community transgresses some predefined critical abundance threshold. In the last paper (Paper IV) the community wide effect of changes in the abundance of species is investigated. In the first paper (Paper I) I investigate threshold levels for the mortality rate of species in ecological networks. When an increased mortality rate of a focal species causes another species to go extinct, the focal species can be characterized as functional extinct, even though it still exists. Such functional extinctions have been observed in a few systems, but their frequency and general patterns have been unexplored. Using a new analytical method the patterns and frequency of functional extinctions in theoretical and empirical ecological networks are explored. It is found that the species most likely to be the first to go extinct is not the species whose mortality rate is increased, but instead another species in the network. The species which goes extinct is often not even directly linked to the species whose mortality rate is increased, but instead indirectly linked. Further, it is found that large-bodied species at the top of food chains can only be exposed to small increases in mortality rate and small decreases in abundance before going functionally extinct compared to small-bodied species lower in the food chains. These results illustrate the potential importance of functional extinctions in ecological networks and lend support to arguments advocating a more community-oriented approach in conservation biology, with target level...

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Quantifying effects of abiotic and biotic drivers on community dynamics with multivariate autoregressive (MAR) models

Cited by 105 publications

References 47 publications

Tracking and forecasting ecosystem interactions in real time

Tracking and forecasting ecosystem interactions in real time

Effects of climate change on zooplankton community interactions in an Alaskan lake

Functional Extinctions of Species in Ecological Networks

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