IRCEB: Building a Mechanistic Basis for Landscape Ecology of Ungulate Populations

DEB-0078130 P. I.：Turchin，Peter IRCEB：空间动态对于理解人口、社区和生态系统过程至关重要。景观生态学中有两个普遍的问题还没有足够的解决方案：（1）将小空间（和时间）尺度上的观测结果转化为景观和地理尺度上的预期模式，（2）了解空间异质性对个体运动/人口统计学以及种群分布和动态的影响。我们建议解决这些问题，使用多方面的研究计划，整合经验数据收集与空间建模和GIS分析。拟议的实验生物是美国麋鹿。该物种是高度保护/自然管理的利益，并在北美的几个麋鹿重新引入的努力目前正在进行中。然而，人们对重新引入的麋鹿种群的传播速度知之甚少。因此，通过成功完成本提案中概述的研究目标，我们不仅能够解决理论生态学的核心问题，而且还将获得改进管理决策的见解。我们对上述两个问题的具体方法将基于开发一系列空间模型，解决一系列尺度，从微尺度（几十米和小时）到中尺度（公里和星期）再到宏观尺度（几十到几百公里和年）。在每个尺度上，我们将使用个人移动和人口统计学的小规模数据来参数化模型，并通过预测更广泛（景观和最终地理）空间尺度上人口空间再分布的独立数据来测试模型。个别运动的数据将获得以下麋鹿配备无线电项圈在四个不同的网站，不同的景观异质性和麋鹿人口密度（两个网站，麋鹿已经建立了很长一段时间，和两个，他们最近推出）。将结合使用遥感方法和对冬季和夏季饲料生物量和质量的分布、土地和生境分类以及景观结构等变量的详细实地分析，对景观异质性进行量化。我们将确定如何准确地微尺度模式可以预测中尺度数据，中尺度模式可以预测宏观尺度数据。此外，详细的，基于机制的中尺度模式的宏观预测将对比从一个更现象学（但少得多的数据要求）的宏观模型的预测。这种比较将使我们能够确定是否有必要了解多个空间尺度上的运动过程，以便在更大的（地理）尺度上对人口再分布进行有意义的预测。

DEB-0078130 P. I.: Turchin, Peter IRCEB: Spatial dynamics are crucial for understanding population, community, and ecosystem processes. Two general problems in landscape ecology do not yet have adequate solutions: (1) translating observations taken at small spatial (and temporal) scales into expected patterns at landscape and geographic scales, and (2) understanding the influences of spatial heterogeneity on individual movements/demography, and on population distribution and dynamics. We propose to address these issues using a multi-faceted research program that integrates empirical data collection with spatial modeling and GIS analyses. The proposed experimental organism is the American elk. This species is of high conservation/natural management interest, and several elk reintroduction efforts in North America are currently in progress. However, little is known about how fast reintroduced elk populations may spread. Thus, by successfully completing the research goals outlined in this proposal we will not only be able to address questions of central importance to theoretical ecology, but will also obtain insights for improved management decisions.Our specific approach to the two issues above will be based on developing a series of spatial models addressing a range of scales, from a microscale (tens of m and hours) through a mesoscale (km and weeks) to a macroscale (tens to hundreds of km and years). At each scale we will parameterize the model using small-scale data on individual movements and demography, and test the model by predicting independent data on spatial redistribution of populations at broader (landscape and, ultimately, geographic) spatial scales. Data on individual movements will be obtained by following elk equipped with radiocollars at four different sites, varying in landscape heterogeneity and elk population density (two sites where elk have been long established, and two where they were recently introduced). Landscape heterogeneity will be quantified using a combination of remote sensing methods and detailed on-the-ground analyses of such variables as the distribution of winter and summer forage biomass and quality, land and habitat classification, and landscape structure. We will determine how accurately microscale model can predict mesoscale data, and mesoscale model can predict macroscale data. Furthermore, the macroscale predictions of the detailed, mechanism-based mesoscale model will be contrasted to the predictions from a more phenomenological (but much less data-demanding) macroscale model. This comparison will allow us to determine whether it is essential to understand processes governing movement at multiple spatial scales in order to make meaningful predictions of population redistribution at larger (geographic) scales.