基于个体的骡鹿(Odocoileus hemionus)接触运动模型

IF 3.1 3区 环境科学与生态学 Q2 ECOLOGY
Kelsey Gritter , Maria Dobbin , Evelyn Merrill , Mark Lewis
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引用次数: 0

摘要

个体之间的接触是传染病传播的关键。虽然接触率对了解疾病传播至关重要,但仅凭野生动物种群的人口统计很难预测接触率,因为接触率取决于环境特征以及个体群体内部和之间的社会互动性质。我们在 Netlogo 中开发了一个详细的、行为结构化的、基于个体的模型(IBM),用于模拟骡鹿(Odocoileus hemionus)个体群体之间和群体内部的接触,骡鹿是一种特别容易感染慢性消耗性疾病的物种。该模型跟踪接触(定义为两个个体相距五米以内),根据两个个体(dyad)的社会群体成员身份记录为群体间或群体内接触。我们利用加拿大艾伯塔省中东部带有全球定位系统(GPS)项圈的骡鹿的数据对模型进行了参数化。骡鹿个体根据对栖息地的偏好、家园范围的吸引力和群居行为进行移动。以两小时的时间步长对动物进行追踪,并根据性别特异性综合步长选择函数(iSSF)模拟动物选择相对于首选资源的位置,步长偏向于家域中心点。群内接触总数随群的大小而增加,并对群的运动凝聚力和运动持久性的变化,特别是运动凝聚力的变化敏感。群间接触总数只对群的数量敏感。我们将模型预测的鹿接触地点与现有的同一地貌相对接触概率(RCP)统计模型进行了比较(Dobbin 等,2023 年)。预测的鹿接触地点通常与较高的预测 RCP 值一致。当疾病传播是接触率的函数时,该模型可用于评估模型组成部分(如移动率、分组规则、家园范围、动物密度)与可能吸引鹿并可能增加疾病传播的关键自然和人工资源的空间分布之间的相互作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An individual-based movement model for contacts between mule deer (Odocoileus hemionus)

Contacts between individuals are key for the spread of infectious disease. Although essential to understanding disease spread, contact rates are difficult to predict, based simply on population demographics in wildlife populations, because contact rates depend upon environmental features as well as the nature of social interactions within and between groups of individuals. We developed a detailed, behaviorally structured, individual-based model (IBM) in Netlogo to simulate contacts between- and within-groups of individual mule deer (Odocoileus hemionus), a species particularly susceptible to chronic wasting disease. The model tracks contacts (defined as two individuals coming within five meters of one another), recorded as between- or within-group depending on the social group membership of the two individuals (dyad). We parameterized the model with data from mule deer with global positioning systems (GPS) collars in east-central Alberta, Canada. Individuals move according to habitat preferences, home range attraction, and grouping behaviours. Animals were tracked at two-hour time steps and were modelled as selecting locations relative to preferred resources based on sex-specific integrated step-selection functions (iSSFs) with steps biased toward a home range centroid. Total within-group contacts increased with group size and were sensitive to changes in movement cohesion of the group and movement persistence, particularly movement cohesion. Total between-group contacts were sensitive only to the number of groups. We compared model predictions for where the locations of deer contacts occurred against an existing statistical model for the relative contact probabilities (RCP) on the same landscape (Dobbin et al. 2023). Predicted locations of deer contacts generally were consistent with higher predicted RCP values. When disease transmission is a function of contact rate, the model can be used to assess the interaction between model components (e.g., movement rates, grouping rules, home ranges, animal densities) and the spatial distribution of key natural and artificial resources that may attract deer and potentially increase disease spread.

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来源期刊
Ecological Complexity
Ecological Complexity 环境科学-生态学
CiteScore
7.10
自引率
0.00%
发文量
24
审稿时长
3 months
期刊介绍: Ecological Complexity is an international journal devoted to the publication of high quality, peer-reviewed articles on all aspects of biocomplexity in the environment, theoretical ecology, and special issues on topics of current interest. The scope of the journal is wide and interdisciplinary with an integrated and quantitative approach. The journal particularly encourages submission of papers that integrate natural and social processes at appropriately broad spatio-temporal scales. Ecological Complexity will publish research into the following areas: • All aspects of biocomplexity in the environment and theoretical ecology • Ecosystems and biospheres as complex adaptive systems • Self-organization of spatially extended ecosystems • Emergent properties and structures of complex ecosystems • Ecological pattern formation in space and time • The role of biophysical constraints and evolutionary attractors on species assemblages • Ecological scaling (scale invariance, scale covariance and across scale dynamics), allometry, and hierarchy theory • Ecological topology and networks • Studies towards an ecology of complex systems • Complex systems approaches for the study of dynamic human-environment interactions • Using knowledge of nonlinear phenomena to better guide policy development for adaptation strategies and mitigation to environmental change • New tools and methods for studying ecological complexity
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