Linking demographic theory and data to forecast the dynamics of spatially-structured seasonally-mobile populations

GSTDTAP

项目编号	NE/R000859/1
	Linking demographic theory and data to forecast the dynamics of spatially-structured seasonally-mobile populations
	Jane Reid
主持机构	University of Aberdeen
项目开始年	2017
	2017-10-01
项目结束日期	2020-05-31
资助机构	UK-NERC
项目类别	Research Grant
国家	英国
语种	英语
英文摘要	Wild populations are facing unprecedented threats. Many habitats are naturally patchy or being fragmented by human activities. Many habitats also show seasonal variation in suitability, and climate models predict increasing frequencies of extreme seasonal weather events (e.g. storms, droughts, floods) that could cause high mortality or prevent reproduction in affected areas. Critical aims in ecology are therefore to understand and forecast the dynamics and persistence of populations inhabiting spatially-structured seasonal environments, including population responses to extreme seasonal events that alter demography. Spatio-temporal population dynamics ultimately depend on four primary demographic rates: survival, reproduction, dispersal (permanent movements among breeding areas) and migration (reversible seasonal movements). However, to date, no empirical studies or population dynamic theory or models have fully quantified or considered spatio-temporal variation in migration alongside variation in survival, dispersal and reproduction. We consequently lack data, theory and models that quantify population dynamic effects of variation in and among all four key demographic rates, severely limiting our ability to forecast the dynamics of spatially-structured seasonally-mobile populations. It is now clear that partial migration, where populations comprise mixtures of resident and migrant individuals, is very common in nature. Partially-migratory populations could show complex demography and dynamics because structured variation in migration could cause structured variation in current or subsequent survival, dispersal or reproduction, while extreme events could cause irruptive migration. Population dynamic models and empirical studies that ignore spatio-temporal variation in migration, and resulting carry-over effects, therefore ignore fundamental axes of demographic variation. We will provide new empirical and theoretical understanding of the demography and dynamics of spatially-structured seasonally-mobile populations, and new capability for population dynamic forecasting, by: 1) Providing first empirical estimates of key demographic rates and relationships that drive population dynamics, including demographic responses to extreme events. We will use an outstanding large-scale multi-year dataset that we have collected on a spatially-structured partially-migratory European shag population. During 2009-2017 we marked 14,500 individuals across 6 breeding colonies, collected 44,200 winter sightings to identify residents and migrants, and measured individual dispersal and reproduction. In 2012-2014, the system experienced extreme winter storms, causing high mortality. This system now provides an unrivalled opportunity to quantify sex-, age- and sub-population-specific variation in individual migration, and covariation with survival, dispersal and reproduction, before, during and after natural extreme seasonal events. 2) Providing new theory that identifies general principles of population dynamic responses to seasonal demographic variation. We will build and analyse individual-based simulation models that quantify population consequences of demographic covariation involving migration. We will quantify sensitivities of population growth rate to hypothesised forms of structured partial migration and covariation with survival, dispersal and reproduction given varying life-histories and spatial habitat structures, and to hypothesised regimes of extreme events and resulting demographic perturbations. 3) Providing a new flexible modelling framework for forecasting spatio-temporal population dynamics. We will build new migration models into our state-of-the-art 'RangeShifter' software for spatially-explicit population dynamic modelling and simulate spatio-temporal dynamics of the exemplar European shag system, thereby demonstrating new flexible forecasting capability and informing conservation policy.
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/86808
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Jane Reid.Linking demographic theory and data to forecast the dynamics of spatially-structured seasonally-mobile populations.2017.