NSFDEB-NERC: Testing effects of resources and competitors at multiple spatial and temporal scales in multiple populations

几乎所有物种都受到与其他物种竞争的影响。 在大多数情况下，一个物种（从属竞争者）比另一个物种（主导竞争者）受到的影响更大，竞争可以限制种群规模，甚至导致从属物种的局部灭绝。 这有两种方式。 最明显的是，有直接的互动，如占主导地位的竞争对手从下属那里偷食物。 不那么明显的是，下属避免直接互动也会付出代价，例如，从一个食物丰富但可能遇到占主导地位的竞争对手的地区搬走。 这项研究将使用尖端技术来测试竞争的直接和间接影响如何影响非洲野狗（Lycaon pictus）的运动，能量的获得和损失，生存和繁殖，以及它们的主要竞争对手狮子（Panthera leo）和猎物数量的高低。 由于世界上许多地区的猎物数量正在减少，了解猎物丰度的变化如何改变竞争的影响对于保护和管理至关重要。人们普遍认为，猎物数量不仅受到直接捕食的限制，而且还受到避免捕食的成本（“风险效应”）的限制。 逻辑表明，竞争互动中也可能存在风险效应。 该项目将测试避免风险是否会带来能量成本，这些成本会转化为对生存，繁殖，种群动态和次要竞争对手非洲野狗基因流的影响。 该项目将把新方法纳入对非洲野狗、狮子和三个生态系统中猎物种群的长期研究。 具体来说，对野狗的直接观察将与来自GPS项圈、高频三轴加速度计和磁场强度传感器的数据相结合，以获得关于野狗在已知狮子和猎物密度和分布的地区狩猎时的运动、动态身体加速度、能量消耗和能量增益的非常精细的数据。 三轴加速度计将提供矢量动态身体加速度（VeDBA）和能量消耗的详细和精确的测量，时间范围从秒到天或月。 全球定位系统项圈将提供从动态布朗桥运动模型（dBB）在从小时到年的时间尺度上对空间使用和运动的推断。dBBb2或其他运动模型拟合到从VeDBA、磁场强度和GPS位置的组合导出的轨迹，将测试对运动的影响，小到秒级。 在连续三天的“跟随”中直接观察相同的个体，将提供与猎物、狩猎和杀戮相遇的时空匹配数据，以量化从数小时到数年的时间尺度上的能量增益，并将为其他数据的解释提供关键背景。 通过将这些数据与对已知个体的密集、长期监测配对，可以使用贝叶斯综合种群模型来测试与生存、繁殖和种群动态的关系，并通过使用已经开发和验证的SNP芯片来理解对基因流的影响。一系列的生态条件下的数据将通过复制的研究，在三个生态系统中的竞争对手和猎物的密度有很好的测量变化收集。 该研究部分由综合有机系统部的行为系统集群共同资助。该项目由环境生物学部和综合有机系统部（BIO）共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Almost all species are affected by competition with other species. In most cases, one species (the subordinate competitor) is more affected than the other (the dominant competitor), and competition can limit the population size or even cause local extinction of subordinate species. This happens in two ways. Most obviously, there are direct interactions such as the dominant competitor stealing food from the subordinate. Less obviously, there are costs to subordinates of avoiding direct interactions, for example, by moving away from an area with abundant food but where the dominant competitor is likely to be encountered. This research will use cutting edge technology to test how direct and indirect effects of competition affect the movements, energy gain and loss, survival, and reproduction of African wild dogs (Lycaon pictus) in areas with high and low numbers of their dominant competitor, the lion (Panthera leo), and with high and low numbers of prey. Because prey populations are declining in many areas of the world, understanding how changes in prey abundance alter the effect of competition is essential for conservation and management.It is widely recognized that prey populations can be limited not only by direct predation, but also by the costs of avoiding predation (‘risk effects’). Logic suggests that risk effects might also exist in competitive interactions. This project will test whether the avoidance of risk carries energetic costs that translate into effects on survival, reproduction, population dynamics, and gene flow in a subordinate competitor, the African wild dog. The project will incorporate new methods into long-term studies of African wild dog, lion, and prey populations in three ecosystems. Specifically, direct observation of wild dogs will be coupled to data from GPS collars, high frequency triaxial accelerometers, and magnetic field intensity sensors to obtain very fine-scaled data on movement, dynamic body acceleration, energy expenditure, and energy gain for wild dogs hunting in areas with known densities and distributions of lions and prey. Triaxial accelerometers will provide detailed and precise measurements of vectorial dynamic body acceleration (VeDBA) and energy expenditure at time scales ranging from seconds to days or months. GPS collars will provide inferences on space use and movement from dynamic Brownian bridge movement models (dBBMMs) at time scales from hours to years. dBBMMs or other movement models fit to trajectories derived from a combination of VeDBA, magnetic field intensity, and GPS locations will test for effects on movement down to the scale of seconds. Direct observation of the same individuals in continuous three-day ‘follows’ will provide spatiotemporally matched data on encounters with prey, hunts, and kills to quantify energy gain at time scales from hours to years, and will provide critical context for the interpretation of other data. By pairing these data with intensive, long-term monitoring of known individuals, relationships with survival, reproduction, and population dynamics can be tested using a Bayesian integrated population model, and effects on gene flow understood by using a SNP chip already developed and validated. Data for a range of ecological conditions will be collected through replication of the study across three ecosystems with well-measured variation in the densities of competitors and prey. This research is co-funded in part by the Behavioral Systems Cluster in the Division of Integrative Organismal Systems.This project is jointly funded between the Division of Environmental Biology and the Division of Integrative Organismal Systems (BIO).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.