The phenological optimum in space and time

GSTDTAP

项目编号	NE/P011802/1
	The phenological optimum in space and time
	Albert Phillimore
主持机构	University of Edinburgh
项目开始年	2017
	2017-04-01
项目结束日期	2021-03-31
资助机构	UK-NERC
项目类别	Research Grant
国家	英国
语种	英语
英文摘要	For the many animals and plants that live in seasonal environments, getting the right timing to key life cycle events is vital. To take insectivorous woodland birds as an example, they must lay their eggs early in spring to ensure that their chicks will hatch and grow when food, such as caterpillars, is most plentiful. Because a full month elapses between starting to lay eggs and the maximum food demand of their chicks, the birds use cues in the environment in early spring, such as temperature, to predict what will happen later in the spring. An individual female can therefore be quite flexible in the timing of egg-laying, advancing the time by about two - five days (depending on the species) if spring temperatures are 1C warmer, an ability that we call plasticity. The prediction that the female makes about future conditions remains imperfect however; chicks will often be reared at the wrong time, resulting in few, if any, fledging. With rising spring temperatures the food peak may advance at a rate that is faster than individual birds can keep up with. Therefore, to keep up a species may need to evolve an earlier egg-laying date or, if this is not possible, it may ultimately go extinct. Predicting how climate change will affect species has proven very difficult. If we know how much the optimum timing will shift for a 1C rise in temperature and we know how much a species can track this shift via plasticity, we can work out the amount of ground that evolution will need to make up. Measuring plasticity of timing in response to temperature, whether it be bird egg-laying, frog spawning, plant flowering or tree leafing, is relatively easy. Much more difficult is to work out the amount by which a species needs to shift its timing to track ideal conditions. In fact this has been done for just a single species, the great tit. We will identify the relationship between spring temperatures and optimum timing for a second species, the blue tit, a close relative of the great tit. We will monitor blue tit egg-laying and the availability of caterpillars in four springs and across 50 sites that stretch north of Edinburgh for 200km. We will conduct a huge experiment, swapping hundreds of clutches between the nest boxes of incubating females, to establish when is the best time to lay. We think that the best laying date for a blue tit is likely to be governed by the relationship between temperature and the peak availability of caterpillars. However, the blue tit, like many species is a habitat and feeding generalist and it is possible that the relationship between spring temperature and the best time to lay will vary from one site to another as different caterpillar species become more or less abundant. We will test this idea and if we find support for it this implies that for generalist species we may need to identify the relationship between temperature and the optimum timing anew every time we consider a different population or habitat. Clearly, we cannot expect this huge investment of time or resources to be expended on more than a handful of taxa and yet we need to know how temperature relates to the optimum timing for many taxa. We have developed a method that can infer this relationship from the type of observations on timings that citizen science projects like Nature's Calendar and the BTO Nest Record Scheme have been so effective at collecting. This could greatly increase the number of species for which we can identify this relationship. However, before we encourage much wider use of this method we need first to improve the statistical method and test whether the assumptions it requires are valid in the blue tit.
来源学科分类	Natural Environment Research
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/86630
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Albert Phillimore.The phenological optimum in space and time.2017.