Ecological and Evolutionary Consequences of Experimental Warming in a High Arctic Tundra Ecosystem
Bibliographic record
Abstract
Over the past 100 years, global temperatures necessary to maintain Arctic species, have risen by an average of 0.85°C (IPCC, 2013). Rapid adaptation to environmental change has already This trend is especially pronounced in the Arctic, been described in some species. The critical photoperiod where temperatures have risen by 2°C over the past 50 years of northern populations of pitcher plant mosquitoes ( Wyeo alone and are expected to rise an additional 2°-5°C by the myia smithii) has shifted towards that of more southern end of this century (ACIA, 2005). This rapid increase in populations, thus lengthening the breeding season for these temperature is expected to have wide-ranging implications populations (Bradshaw and Holzapfel, 2001). In the Yukon, for Arctic ecosystems, including changes in biodiversity, evolutionary adaptation accounted for 13% of an observed ecosystem functioning, and nutrient cycles. The future of shift in parturition date for red squirrels (Tamiasciurus hud Arctic ecosystems will depend on three factors: the extent sonicus) (62% was a result of phenotypic plasticity) (Reale to which individuals can adjust to warmer temperatures et al., 2003; Berteaux et al., 2004). In plants, evolution in through phenotypic plasticity, the rate of immigration of response to increased drought was detected in a population species from southern latitudes, and the rate at which evoof Brassica rapa after only a few generations (Franks et lutionary adaptation at the species level can take place in a al., 2007). However, the vast majority of studies describing rapidly changing environment (Aitken et ah, 2008; Gienapp observed trait shifts in response to climate change provide et ah, 2008). In essence, if species cannot adjust to warmer no evidence of whether these shifts are plastic or adaptive temperatures in situ (phenotypic plasticity), they must (Parmesan and Yohe, 2003; Gienapp et ah, 2008). move, adapt, or die. Migration in response to warming temperatures has also Widespread changes in the Arctic are already underbeen widely documented. In the United Kingdom, 63% of way. Recent syntheses of plant community composition evaluated butterfly species have experienced northward data have shown that some functional groups, particurange shifts over the past century (Parmesan et ah, 1999). larly shrubs and graminoids, have responded positively to Similarly, British bird species have experienced an aver warming, while others, including lichens, have declined age northward range shift of 18.9 km (Thomas and Len (Elmendorf et ah, 2012). This shrubification of the Arctic non, 1999). In the Arctic, a majority of surveyed sites show is likely to have important consequences for the herbivore evidence of northward tree line advancement (Harsch et community and to alter snow distribution, duration, and ah, 2009). In a meta-analysis of data from 1700 plant and albedo effects (Myers-Smith et ah, 2011). Individual species animal species worldwide, Parmesan and Yohe (2003) have also shown changes in response to warming. Plants described an average northward range shift of 6.1 km (or in areas of rapid warming often respond by flowering and 6.1 m upward in elevation) per decade across all species, senescing earlier, although responses vary substantially by Although migration is perhaps the most widely discussed location and growth form (Oberbauer et ah, 2013). of the three climate-change responses, it is far from cer Despite a growing body of evidence that plants are tain that species will be able to track their optimal climate changing in response to warming temperatures, little is northward as the climate warms. Predicted rates of future known about the mechanisms behind these changes. Clasclimate change are much greater than those of historical sical studies of Arctic species have demonstrated that changes; species will therefore be required to track changes although individual populations show a high degree of in climate at speeds 100 times those of historical migra phenotypic plasticity, adaptation to local conditions was tions (Davis, 1989; Aitken et al., 2008). In addition, poten also widespread (Mooney and Billings, 1961). This genetic tial migration pathways have been considerably fragmented diversity within the species as a whole could become by human land use, especially agriculture and residential important as environmental conditions change. If plastic settlement (McCarty, 2001). These obstacles represent a responses are not sufficient to keep up with the rapid rise in further barrier to species dispersal and migration. Finally,
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".