A spectral and <scp>B</scp>ayesian approach for analysis of fluctuations and synchrony in ecological datasets

Lindström, Tom; Sisson, Scott A.; Håkansson, Nina; Bergman, Karl‐Olof; Wennergren, Uno

doi:10.1111/j.2041-210x.2012.00240.x

Cited by 9 publications

(14 citation statements)

References 32 publications

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“…The hierarchical Bayesian framework allows us to compare populations, while acknowledging parameter uncertainty at the individual level (27). In combination with the hidden states model, this approach avoids confusing differences between individuals with different behavioral modes within an individual.…”

Section: Discussionmentioning

confidence: 99%

Rapid shifts in dispersal behavior on an expanding range edge

Lindström

Brown

Sisson

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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128

118

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Dispersal biology at an invasion front differs from that of populations within the range core, because novel evolutionary and ecological processes come into play in the nonequilibrium conditions at expanding range edges. In a world where species' range limits are changing rapidly, we need to understand how individuals disperse at an invasion front. We analyzed an extensive dataset from radio-tracking invasive cane toads (Rhinella marina) over the first 8 y since they arrived at a site in tropical Australia. Movement patterns of toads in the invasion vanguard differed from those of individuals in the same area postcolonization. Our model discriminated encamped versus dispersive phases within each toad's movements and demonstrated that pioneer toads spent longer periods in dispersive mode and displayed longer, more directed movements while they were in dispersive mode. These analyses predict that overall displacement per year is more than twice as far for toads at the invasion front compared with those tracked a few years later at the same site. Studies on established populations (or even those a few years postestablishment) thus may massively underestimate dispersal rates at the leading edge of an expanding population. This, in turn, will cause us to underpredict the rates at which invasive organisms move into new territory and at which native taxa can expand into newly available habitat under climate change.hierarchical Bayes | shift | spatial sorting | relocation data | hidden states M any populations are shifting their range edges in response to climate change (1, 2) and other anthropogenic stressors (3, 4), and invasive species are spreading rapidly worldwide (5, 6). We need to predict rates of range shift to manage these changes, but (despite sophisticated theory on the processes underlying range shift), models routinely underestimate the rate of spread (7) because they underestimate the frequency of longdistance dispersal. Not only are rare long-distance dispersal events difficult to document (8, 9), but populations in the spreading vanguard are subject to powerful ecological and evolutionary forces not experienced by conspecifics within the range core. On the expanding range edge, individuals mate assortatively by dispersal ability ("spatial sorting") (10, 11); deleterious mutations can surf to fixation (12, 13), and parasites and pathogens may get left behind (14, 15). These differences could all affect dispersal rates and mean that to predict rates of range shift, we need to actually measure dispersal at the invasion front.Understanding the differences in dispersal rates at and behind an invasion front also may clarify the roles of plasticity, evolution, and ecology. For example, covariation of dispersal rates with conspecific or pathogen density implies a role for plasticity (e.g., density-dependent dispersal) or ecology (the effect of parasites and pathogens) rather than evolutionary processes in driving dispersal-rate variation. In contrast, higher dispersal rates at the invasion front versus further ...

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Section: Discussionmentioning

confidence: 99%

Rapid shifts in dispersal behavior on an expanding range edge

Lindström

Brown

Sisson

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

128

118

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“…Tree rings were identified and dated by counting them from bark to pith with the help of a stereomicroscope. The raw tree-ring widths of each dated core were visually checked using time series analysis and presentation (TSAP) [28,29], and then synchronized according to the year-to-year agreement between the interval trends of two chronologies based on the sign of agreement. Then the tree ring widths ( Fig.…”

Section: Treatment Of the Samplesmentioning

confidence: 99%

The Response of Chinese Fir Forest Tree Ring Growth to Climate Change in China’s Dagangshan Region

Liu¹,

Gao²,

Panwei³

et al. 2019

Pol. J. Environ. Stud.

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Chinese fir (Cunninghamia lanceolata (Lamb) Hook.) in the Dagangshan region of Jiangxi Province in southern China was selected to explore the impact of climatic factors on tree ring width growth. Results showed that the signal-to-noise ratio (SNR) and mean sensitivity (MS) were large while the first order autocorrelation coefficient (FOAC) was small, and the chronology contains abundant climatic information. The tree ring width index of Chinese fir was significantly positively correlated with the precipitation in December of the previous year and June of the current year, and significantly negatively correlated with that in the current May and August. The tree ring width index was significantly positively correlated with the temperature in April and May of the current year, but significantly negatively correlated with that in July of the current year. The tree ring width index had a good change consistency with the warmth index (W i ) and humidity index (H i ). The response function of Chinese fir ring width analysis shows that the warmth index is the main factor affecting tree ring growth, followed by the humidity index, mean annual precipitation, and mean annual temperature. The comprehensive effect of temperature and precipitation factors have significant influence on the tree ring width growth of Chinese fir. Results can provide a scientific basis for studying the effect of climatic factors on tree growth in sub-tropical regions in China and many other parts of the world.

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“…In our experimental exploration of the enhanced Moran effect, we covered the wide range of spatial variation in autocorrelation that is known from empirical studies of environmental variables, including air and water temperatures in and around lakes, rivers and oceans 24,26,29,[39][40][41] . Organisms distributed over broad ranges or even on a global scale are very likely to be encountered in such habitats.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, red noise indicates positive autocorrelation, that is, successive values are more similar than expected by chance. Time series with red noise are dominated by low-frequency fluctuations and are often found in nature, for example, in climate variables and population densities [24][25][26][27][28][29] . The colour of environmental noise can have a substantial influence on population dynamics and persistence 26,[30][31][32][33][34][35] but, although relevant, its effect on population synchrony has rarely been addressed.…”

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confidence: 99%

“…Previous theoretical investigations revealed that reddened environmental noise may intensify spatial synchrony 19,30,[36][37][38] . These investigations, however, were restricted to the case of identical noise colours in all populations, despite the fact that empirical studies of a wide range of environmental variables have revealed substantial variations in noise colour between geographic regions and on gradients from land to the ocean 24,26,29,[39][40][41] .…”

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Enhanced Moran effect by spatial variation in environmental autocorrelation

Massie

Weithoff

Kuckländer³

et al. 2015

Nat Commun

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Spatial correlations in environmental stochasticity can synchronize populations over wide areas, a phenomenon known as the Moran effect. The Moran effect has been confirmed in field, laboratory and theoretical investigations. Little is known, however, about the Moran effect in a common ecological case, when environmental variation is temporally autocorrelated and this autocorrelation varies spatially. Here we perform chemostat experiments to investigate the temporal response of independent phytoplankton populations to autocorrelated stochastic forcing. In contrast to naive expectation, two populations without direct coupling can be more strongly correlated than their environmental forcing (enhanced Moran effect), if the stochastic variations differ in their autocorrelation. Our experimental findings are in agreement with numerical simulations and analytical calculations. The enhanced Moran effect is robust to changes in population dynamics, noise spectra and different measures of correlation-suggesting that noise-induced synchrony may play a larger role for population dynamics than previously thought.

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A spectral and Bayesian approach for analysis of fluctuations and synchrony in ecological datasets

Cited by 9 publications

References 32 publications

Rapid shifts in dispersal behavior on an expanding range edge

Rapid shifts in dispersal behavior on an expanding range edge

The Response of Chinese Fir Forest Tree Ring Growth to Climate Change in China’s Dagangshan Region

Enhanced Moran effect by spatial variation in environmental autocorrelation

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