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Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement
Ecological Modelling ( IF 2.6 ) Pub Date : 2021-03-09 , DOI: 10.1016/j.ecolmodel.2021.109499
Stephanie G. Diaz , Donald L. DeAngelis , Michael S. Gaines , Andrew Purdon , Michael A. Mole , Rudi J. van Aarde

African elephants (Loxodonta africana) are well-studied and inhabit diverse landscapes that are being transformed by both humans and natural forces. Most tools currently in use are limited in their ability to predict how elephants will respond to novel changes in the environment. Individual-, or agent-based modeling (ABM), may extend current methods in addressing and predicting spatial responses to environmental conditions over time. We developed a spatially explicit agent-based model to simulate elephant space use and validated the model with movement data from elephants in Kruger National Park (KNP) and Chobe National Park (CNP). We simulated movement at an hourly scale, as this scale can reflect switches in elephant behavior due to changes in internal states and short-term responses to the local availability and distribution of critical resources, including forage, water, and shade. Known internal drivers of elephant movement, including perceived temperature and the time since an individual last visited a water source, were linked to the external environment through behavior-based movement rules. Simulations were run on model landscapes representing the wet season and the hot, dry season for both parks. The model outputs, including home range size, daily displacement distance, net displacement distance, and maximum distance traveled from a permanent water source, were evaluated through qualitative and quantitative comparisons to actual elephant movement data from both KNP and CNP. The ABM was successful in reproducing the differences in daily displacements between seasons in each park, and in distances traveled from a permanent water source between parks and seasons. Other movement characteristics, including differences in home range sizes and net daily displacements, were partially reproduced. Out of the all the statistical comparisons made between the empirical and simulated movement patterns, the majority were classified as discrepancies of medium or small effect size. We have shown that a resource-driven model with relatively simple decision rules generates trajectories with movement characteristics that are mostly comparable to those calculated from empirical data. Simulating hourly movement (as our model does) may be useful in predicting how finer-scale patterns of space use, such as those created by foraging movements, are influenced by finer spatio-temporal changes in the environment.



中文翻译:

基于活动决定因素的非洲大草原象(Loxodonta africana)空间利用的基于空间显式代理的模型的开发和验证

非洲象(非洲象)经过深入研究并居住在人类和自然力量共同改造的多种景观中。当前使用的大多数工具在预测大象将如何响应环境中的新变化方面的能力有限。基于个体或基于代理的建模(ABM)可能会扩展当前的方法,以解决和预测随着时间推移对环境条件的空间响应。我们开发了基于空间显式主体的模型来模拟大象的空间使用,并使用来自克鲁格国家公园(KNP)和乔贝国家公园(CNP)的大象的运动数据验证了该模型。我们以小时为单位模拟了运动,因为该比例可以反映由于内部状态的变化以及对本地资源和关键资源(包括草料,水和阴影)的短期响应的短期反应而导致的大象行为变化。已知的大象运动的内部驱动因素,包括感知的温度和自一个人上次访问水源以来的时间,都通过基于行为的运动规律与外部环境联系在一起。在代表两个公园的湿季和干热季的模型景观上进行了模拟。通过对来自KNP和CNP的实际大象运动数据进行定性和定量比较,评估了模型输出,包括居家范围大小,每日位移距离,净位移距离和从永久水源经过的最大距离。ABM成功地再现了每个公园不同季节之间每日排量的差异以及公园与季节之间从永久性水源流走的距离。其他运动特征 包括家庭范围大小和每日净排水量的差异在内,已部分复制。在经验和模拟运动模式之间进行的所有统计比较中,大多数被归类为中等或较小效应大小的差异。我们已经表明,具有相对简单的决策规则的资源驱动模型会生成具有运动特征的轨迹,这些轨迹与可从经验数据中计算出的轨迹具有可比性。模拟每小时运动(如我们的模型所做的那样)可能有助于预测更精细的空间使用模式,例如觅食运动所产生的空间使用模式如何受到环境中更精细的时空变化的影响。在经验和模拟运动模式之间进行的所有统计比较中,大多数被归类为中等或较小效应大小的差异。我们已经表明,具有相对简单的决策规则的资源驱动模型会生成具有运动特征的轨迹,这些轨迹与可从经验数据中计算出的轨迹具有可比性。模拟每小时运动(如我们的模型所做的那样)可能有助于预测更精细的空间使用模式,例如觅食运动所产生的空间使用模式如何受到环境中更精细的时空变化的影响。在经验和模拟运动模式之间进行的所有统计比较中,大多数被归类为中等或较小效应大小的差异。我们已经表明,具有相对简单的决策规则的资源驱动模型会生成具有运动特征的轨迹,这些轨迹与可从经验数据中计算出的轨迹具有可比性。模拟每小时运动(如我们的模型所做的那样)可能有助于预测更精细的空间使用模式,例如觅食运动所产生的空间使用模式如何受到环境中更精细的时空变化的影响。我们已经表明,具有相对简单的决策规则的资源驱动模型会生成具有运动特征的轨迹,这些轨迹与可从经验数据中计算出的轨迹具有可比性。模拟每小时运动(如我们的模型所做的那样)可能有助于预测更精细的空间使用模式,例如觅食运动所产生的空间使用模式如何受到环境中更精细的时空变化的影响。我们已经表明,具有相对简单的决策规则的资源驱动模型会生成具有运动特征的轨迹,这些轨迹与可从经验数据中计算出的轨迹具有可比性。模拟每小时运动(如我们的模型所做的那样)可能有助于预测更精细的空间使用模式,例如觅食运动所产生的空间使用模式如何受到环境中更精细的时空变化的影响。

更新日期:2021-03-09
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