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The city changes the daily activity of urban adapters: Camera-traps study of Apodemus agrarius behaviour and new approaches to data analysis

机译:这座城市改变了城市适配器的日常活动:黑线姬鼠行为的相机陷阱研究和数据分析的新方法

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As a result of the widespread use of camera-traps, the analysis of the daily activity of animals based on field data has become a common practice, which is addressed in ecological studies. The more frequent consideration of this issue in ecological research, however, has not led to any advancement in the techniques of analysis of these activity patterns. In this work, we have two main aims: ecological and methodological. Firstly, using camera-traps in the winter period, we examine the differences in the daily activity of wild small mammal populations, which are affected or unaffected by urbanization; we treated changes in daily activity as indicators of species adaptation to urban conditions. Secondly, we test four different approaches to data analysis regarding the determination and comparison of activity patterns, which are not based on the traditional methods that have been used to date, such as particle swarm optimization (PSO), neural networks, decision trees and cluster analysis. We found that the urbanized environment modifies the daily activity patterns of the mammals studied. Animals from the urban population have a longer active period than their rural counterparts and can forage under more favourable thermal conditions, so that the energetic cost of foraging is lower. PSO and neural networks allow for a more detailed analysis of patterns of daily activity than traditional methods, and their results correspond well with each other. Daily activity analysis shows great potential in the application of new statistical approaches that could supplement and enrich the traditionally used methods (e.g. the kernel density estimation). Our approach may help researchers to gain a broader perspective during their analysis of daily activity patterns and lead to a better description of the ecology of the species or even to more balanced wildlife management decisions.
机译:由于相机陷阱的广泛使用,基于野外数据对动物的日常活动进行分析已成为一种常见的做法,在生态学研究中得到解决。然而,在生态研究中更频繁地考虑这个问题并没有导致对这些活动模式的分析技术的任何进步。在这项工作中,我们有两个主要目标:生态学和方法论。首先,我们在冬季使用相机陷阱,研究了受城市化影响或不受城市化影响的野生小型哺乳动物种群日常活动的差异;我们将日常活动的变化视为物种适应城市条件的指标。其次,我们测试了四种与活动模式的确定和比较有关的数据分析方法,这些方法都不基于迄今为止使用的传统方法,例如粒子群优化(PSO),神经网络,决策树和聚类分析。我们发现城市化环境改变了所研究哺乳动物的日常活动模式。与城市人口相比,城市人口的动物有更长的活动期,并且可以在更有利的温度条件下觅食,因此觅食的能源成本较低。与传统方法相比,PSO和神经网络可以对日常活动的模式进行更详细的分析,并且它们的结果彼此之间具有很好的对应性。日常活动分析显示了在应用新的统计方法方面的巨大潜力,这些方法可以补充和丰富传统使用的方法(例如,核密度估计)。我们的方法可能有助于研究人员在分析日常活动模式时获得更广阔的视野,从而更好地描述该物种的生态,甚至做出更加平衡的野生动植物管理决策。

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