There is more to climate than the North Atlantic Oscillation: a new perspective from climate dynamics to explain the variability in population growth rates of a long-lived seabird

Mesquita, Michel D. S.; Erikstad, Kjell Einar; Sandvik, Hanno; Reiertsen, Tone Kirstin; Barrett, Robert T.; Anker‐Nilssen, Tycho; Hodges, Kevin I.; Bader, Jürgen

doi:10.3389/fevo.2015.00043

Cited by 20 publications

(20 citation statements)

References 39 publications

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“…Our aim is to extend climwin to include a variety of methods and provide the tools and benchmarks to compare them, as the question of what constitutes the best method may depend on the biological question (Teller et al 2016; this study). Another interesting avenue would be to adapt our approach to the question of over which spatial window one should aggregate environmental predictors (Mesquita et al 2015), as for species moving between various locations, the locations at which the weather influence is strongest may in fact need to be determined (note that climwin can already incorporate weather data from different locations in a single model, see Appendix S2).…”

Section: Performance Of Our Approach and Sample Size Considerationsmentioning

confidence: 99%

Identifying the best climatic predictors in ecology and evolution

et al. 2016

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Summary1. Ecologists and many evolutionary biologists relate the variation in physiological, behavioural, life-history, demographic, population and community traits to the variation in weather, a key environmental driver. However, identifying which weather variables (e.g. rain, temperature, El Niño index), over which time period (e.g. recent weather, spring or year-round weather) and in what ways (e.g. mean, threshold of temperature) they affect biological responses is by no means trivial, particularly when traits are expressed at different times among individuals.2. A literature review shows that a systematic approach for identifying weather signals is lacking and that the majority of studies select weather variables from a small number of competing hypotheses that are founded on unverified a priori assumptions. This is worrying because studies that investigate the nature of weather signals in detail suggest that signals can be complex. Using suboptimal or wrongly identified weather signals may lead to unreliable projections and management decisions. 3. We propose a four-step approach that allows for more rigorous identification and quantification of weather signals (or any other predictor variable for which data are available at high temporal resolution), easily implementable with our new R package 'climwin'. We compare our approach with conventional approaches and provide worked examples. 4. Although our more exploratory approach also has some drawbacks, such as the risk of overfitting and bias that our simulations show can occur at low sample and effect sizes, these issues can be addressed with the right knowledge and tools. 5. By developing both the methods to fit critical weather windows to a wide range of biological responses and the tools to validate them and determine sample size requirements, our approach facilitates the exploration and quantification of the biological effects of weather in a rigorous, replicable and comparable way, while also providing a benchmark performance to compare other approaches to.

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Section: Performance Of Our Approach and Sample Size Considerationsmentioning

confidence: 99%

Identifying the best climatic predictors in ecology and evolution

et al. 2016

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“…Our published forensic analysis explained in detail the climate dynamics mechanism that accounted for the death of common guillemots in the winter of 1986–7, the population dynamics of common guillemots, and the role of the atmosphere in modulating changes in the ocean and ecosystems 3 . Simply put, we concluded that there seems to be an interaction between climate and prey stocks, which caused this collapse.…”

Section: Case Closed?mentioning

confidence: 88%

“…The four plots display the correlation coefficient calculated as follows: (A) MSLP and raw r; (B) MSLP and r with the winter of 1986–7 removed from both series; (C) MSLP and detrended r; (D) MSLP and detrended r with the winter of 1986–7 removed from both series. Source: Mesquita et al 3 …”

Section: Climate Analysis Toolboxmentioning

confidence: 99%

Cold Case: The Death of Common Guillemots in the Barents Sea

Mesquita

Erikstad²

2016

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“…Therefore, statistical results should be supported by physical mechanisms (Mesquita et al. ). However, it often difficult to find the underlying mechanism by which the NAO influences ecological processes, especially for terrestrial plants, for which the mechanisms are not always clear (Ottersen et al.…”

Section: Introductionmentioning

confidence: 98%

The Arctic oscillation, climatic variability, and biotic factors influenced seedling dynamics in a Caribbean moist forest

McLaren

Monroe

Wilson

2016

Ecology

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We assessed the influence of the Arctic oscillation (AO) on local climate (using data from 2004 to 2009), their influence and the effects of heterospecific density on seedling dynamics (from January 2006 to August 2009), using data from 120 25-m subplots established in a moist tropical forest over limestone in Jamaica. The AO index (AOI) had a positive nonlinear relationship with mean monthly rainfall and the number of days with rain. Also, there was a significant increase in mean monthly atmospheric temperature in 2006, which coincided with a global temperature increase. Overall, at the community level, as temperature increased, mortality increased and then decreased. Also, mortality was significantly lower in plots with higher densities and those that experienced the positive phase of the AO. The effect of the AO on relative growth rate (RGR) of height (RGRh) varied as the AOI increased from negative to positive, while the number of days with rainfall had a positive effect on recruitment. However, these relationships differed during three six-month and two 12-month sample periods. There was a drought during the first period (dry season) during the negative phase of the AO; consequently, mortality was highest during this period. As the AOI increased (negative to positive), both mortality and RGRh declined while recruitment increased, culminating in a high-recruitment event. In addition, as the number of days with rainfall increased, RGR of diameter (RGRd) values were more positive (indicating that moisture stress was alleviated). During the second period (wet season), mortality increased as seedling density increased (possibly due to increased competition). Additionally, elevated temperature had a significant negative effect on RGRh (again, possibly due to increased competition or due to elevated respiratory carbon loss at higher growth temperatures). After the first two censuses, temperature and the AO influenced dynamics marginally, and seedling heterospecific density became increasingly important (lower mortality at higher densities). At the population level, the number of days with rainfall was the most frequent predictor of dynamics followed by temperature, AO, density and rainfall, and they were largely beneficial.

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There is more to climate than the North Atlantic Oscillation: a new perspective from climate dynamics to explain the variability in population growth rates of a long-lived seabird

Cited by 20 publications

References 39 publications

Identifying the best climatic predictors in ecology and evolution

Identifying the best climatic predictors in ecology and evolution

Cold Case: The Death of Common Guillemots in the Barents Sea

The Arctic oscillation, climatic variability, and biotic factors influenced seedling dynamics in a Caribbean moist forest

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