Consequently, relationships based on short-term data may not properly predict wildlife responses to climate change. Descamps et al., 2017 Mysterud et al., 2001), whereas non-linearity and non-stationarity are difficult do address with short time series. For instance, relationships between environmental parameters and wildlife populations are very likely neither linear nor stationary (e.g. Atlantic puffin series#Such paucity of long-term time series hampers especially our understanding of climate effects on long-lived species, such as seabirds or other top-predators, for which most available time series only cover one or few generations. However, the number of sufficiently long ecological time series is limited (but see e.g. Investigating the ecological effects of changes in climate therefore requires even longer datasets. Effects at more basal levels of the trophic pyramid can, in turn, affect species at more apical trophic levels (Carroll et al., 2016).Īccording to the classical definition, climate is a statistical description of weather over a 30-year period (e.g. Such thermal performance curves are well documented in ectotherms, such as fish (e.g. Thermal performance curves of species determine their responses to global warming, which can create a mismatch between the temperature optimum and the temperatures experienced (McKenzie et al., 2020). In light of projected climatic change due to global warming (IPCC, 2013), this raises concerns for the viability of populations that respond negatively to increasing temperatures, either directly or mediated by effects at lower trophic levels (Jones et al., 2018 Sandvik et al., 2014 Trathan et al., 2020). The best supported models indicate that the population's decline is at least partially caused by the increasing SST around Iceland.Ĭhanges in climatic conditions can have profound effects on the demography, phenology and population trajectories of marine top predators such as seabirds (Dias et al., 2019 Jenouvrier, 2013 Oro, 2014 Sydeman et al., 2015). There is also evidence supporting non-stationarity: The SST at which puffins production peaked has increased by 0.24☌ during the 20th century, although the increase in average SST during the same period has been more than three times faster. Most of the variation (72%) in chick production is explained by a model in which productivity peaks at an SST of 7.1☌, clearly rejecting the assumption of a linear relationship. The sign of decennial correlations switches three times during this period, where the phases of strong negative correlations between puffin productivity and SST correspond to the early 20th century Arctic warming period and to the most recent decades. By estimating an annual chick production index for 128 years, we found prolonged periods of strong correlations between local sea surface temperature (SST) and chick production. It originates in the world's largest puffin colony, in southwest Iceland, which has recently experienced a strong decline. Here we present a harvest time series of Atlantic puffins ( Fratercula arctica) that goes back as far as 1880. Most demographic time series are too short to study the effects of climate on wildlife in the classical sense of meteorological patterns over at least 30 years. An understanding of the underlying mechanisms may be hampered by the non-linearity and non-stationarity of the relationships between temperature and demography, and by the insufficient length of available time series. The current warming of the oceans has been shown to have detrimental effects for a number of species.
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