Trophic control changes with season and nutrient loading in lakes

Rogers, Tanya L.; Munch, Stephan B.; Stewart, Simon D.; Palkovacs, Eric P.; Girón‐Nava, Alfredo; Matsuzaki, Shin‐ichiro S.; Symons, Celia C.

doi:10.1111/ele.13532

Cited by 39 publications

(46 citation statements)

References 73 publications

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“…To assess whether change in body size more strongly influenced changes in density, or vice versa, we used Convergent Cross Mapping (or CCM, (Sugihara et al 2012;Rogers et al 2020)) on the density and body size time series. CCM quantifies the degree to which one time series causally influences another one by estimating how much information of the one is contained in the other (Sugihara et al 2012): if a variable X causally influences another variable Y, but Y does not influence X, we should expect Y to contain information about X, but not the other way around.…”

Section: Time-series Analysismentioning

confidence: 99%

Feedbacks between size and density determine rapid eco-phenotypic dynamics

Gibert

Han

Wieczynski

et al. 2021

Preprint

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1. Body size is a fundamental trait linked to many ecological processes-from individuals to ecosystems. Although the effects of body size on metabolism are well-known, how body size influences, and is influenced by, population growth and density is less clear. Specifically, 1) whether body size, or population dynamics, more strongly influences the other, and, 2) whether observed changes in body size are due to plasticity or rapid evolutionary change, are not well understood. 2. Here, we address these two issues by experimentally tracking population density and mean body size in the protist Tetrahymena pyriformis as it grows from low density to carrying capacity. We then use state-of-the-art time-series analyses to infer the direction, magnitude, and causality of the link between body size and ecological dynamics. Last, we fit two alternative dynamical models to our empirical time series to assess whether plasticity or rapid evolution better explains changes in mean body size. 3. Our results clearly indicate that changes in body size precede and determine changes in population density, not the other way around. We also show that a model assuming that size changes via plasticity more parsimoniously explains these observed coupled phenotypic and ecological dynamics than one that assumes rapid evolution drives changes in size. 4. Together these results suggest that rapid, plastic phenotypic change not only occurs well within ecological timescales but may even precede -and causally influence- ecological dynamics. Furthermore, large individuals may be favored and fuel high population growth rates when population density is low, but smaller individuals may be favored once populations reach carrying capacity and resources become scarcer. Thus, rapid plastic changes in functional traits may play a fundamental and currently unrecognized role in familiar ecological processes like logistic population growth.

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Section: Time-series Analysismentioning

confidence: 99%

Feedbacks between size and density determine rapid eco-phenotypic dynamics

Gibert

Han

Wieczynski

et al. 2021

Preprint

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“…See Movie S1 of (Ye et al., 2015) for an explanation. EDM is increasingly used in ecology to forecast population size (Perretti et al., 2013), identify causal connections (Sugihara et al., 2012), quantify species interactions (Deyle et al., 2016; Rogers et al., 2020) and identify chaotic dynamics (Sugihara, 1994; Sugihara & May, 1990). However, EDM requires long time series and frequent sampling to fully resolve the attractor.…”

Section: Introductionmentioning

confidence: 99%

Leveraging spatial information to forecast nonlinear ecological dynamics

2020

Self Cite

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There has been a recent demand for forecasting in ecology, particularly in the field of ecosystem management. Empirical dynamic modelling (EDM), an equation‐free nonlinear forecasting method, is receiving growing attention, but it requires long time series to produce accurate predictions. Though most ecological time series are short, spatial replicates are often available. Here we explore how utilizing available spatial data can improve our ability to forecast ecological dynamics. There are several ways to incorporate spatial information into EDM and not all have been applied in ecology. We compare spatial EDM approaches used in ecology and physics and introduce a flexible Bayesian model that makes use of prior movement information. We test these methods on simulated data generated with three population dynamics models with varying levels of complexity, time series length, spatial symmetry and heterogeneity. Adding spatial data generally improves accuracy, though the best method depends on the spatial process. We applied the methods to empirical fisheries data, highlighting the complexity of real population dynamics. Leveraging spatial data is an effective way to overcome the problem of short ecological time series. Since the best forecasting method depends on the underlying dynamics, we suggest that users apply several in concert and that this may be useful in identifying spatial heterogeneity in dynamics.

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“…Lake food webs are increasingly experiencing a large variety of environmental changes that can take the form of either pulse or press perturbations, such as short-and long-term temperature changes (IPCC 2013, Seneviratne et al 2014, storms (Stockwell et al 2020), salinization (Kaushal et al 2018), or altered biogeochemical cycles (Kritzberg et al 2019). Response patterns of multiple trophic levels to other disturbances are likely to be different because of the different nature or intensity of the disturbance, but we may also expect similar temporal and spatial response dependencies for other bottom-up and top-down perturbations (e.g., see Rogers et al 2020). Because many direct and indirect, abiotic (e.g., trophic status, temperature, dissolved organic carbon or light climate) and biotic factors (e.g., initial standing stock, biodiversity, or community trait composition) can potentially constrain local responses, further studies could focus on deciphering mechanisms driving site-to-site response differences by employing integrative statistical modeling (e.g., structural equation modeling; Grace et al 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Response patterns of multiple trophic levels to other disturbances are likely to be different because of the different nature or intensity of the disturbance, but we may also expect similar temporal and spatial response dependencies for other bottom-up and top-down perturbations (e.g., see Rogers et al 2020). Since many direct and indirect, abiotic (e.g., trophic status, temperature, dissolved organic carbon or light climate) and biotic factors (e.g., initial standing stock, biodiversity or…”

Section: Accepted Articlementioning

confidence: 99%

Functionally reversible impacts of disturbances on lake food webs linked to spatial and seasonal dependencies

Urrutia‐Cordero

Langenheder

Striebel

et al. 2021

Ecology

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Increasing human impact on the environment is causing drastic changes in disturbance regimes and how they prevail over time. Of increasing relevance is to further our understanding on biological responses to pulse disturbances (short duration) and how they interact with other ongoing press disturbances (constantly present). Because the temporal and spatial contexts of single experiments often limit our ability to generalize results across space and time, we conducted a modularized mesocosm experiment replicated in space (5 lakes along a latitudinal gradient in Scandinavia) and time (2 seasons, spring and summer) to generate general predictions on how the functioning and composition of multi-trophic plankton communities (zoo-, phyto-and bacterioplankton) respond to pulse disturbances acting either in isolation or combined with press disturbances. As pulse disturbance, we used short-term changes in fish presence and as press disturbance, we addressed the ongoing reduction in light availability caused by increased cloudiness and lake browning in many boreal and subarctic lakes. First, our results show that the top-down, pulse disturbance had the strongest effects on both functioning and composition of the three trophic levels across sites and seasons, with signs for interactive impacts with the bottom-up, press disturbance on phytoplankton communities. Second, community composition responses to disturbances were highly divergent between lakes and seasons: temporal accumulated community turnover of the same trophic level either increased (destabilization) or decreased (stabilization) in response to the disturbances compared to control conditions. Third, we found functional recovery from the pulse disturbances to be frequent at the end of most experiments. In a broader context, these results demonstrate that top down, pulse disturbances, either alone or with additional constant stress upon primary producers caused by bottom-up disturbances, can induce profound but often functionally reversible changes across multiple trophic levels, which are strongly linked to spatial and temporal context dependencies. Furthermore, the identified dichotomy of disturbance effects on the turnover in community composition demonstrates the potential of disturbances to either stabilize or destabilize biodiversity patterns over time across a wide range of environmental conditions.

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Trophic control changes with season and nutrient loading in lakes

Cited by 39 publications

References 73 publications

Feedbacks between size and density determine rapid eco-phenotypic dynamics

Feedbacks between size and density determine rapid eco-phenotypic dynamics

Leveraging spatial information to forecast nonlinear ecological dynamics

Functionally reversible impacts of disturbances on lake food webs linked to spatial and seasonal dependencies

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