根据稀疏的运动数据估算麋鹿迁徙走廊

IF 2.7 3区 环境科学与生态学 Q2 ECOLOGY
Ecosphere Pub Date : 2024-09-18 DOI:10.1002/ecs2.4983
Jennifer L. McKee, Julien Fattebert, Ellen O. Aikens, Jodi Berg, Scott Bergen, Eric K. Cole, Holly E. Copeland, Alyson B. Courtemanch, Sarah Dewey, Mark Hurley, Blake Lowrey, Jerod A. Merkle, Arthur D. Middleton, Tristan A. Nuñez, Hall Sawyer, Matthew J. Kauffman
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引用次数: 0

摘要

许多有蹄类动物在不同的夏季和冬季牧场之间迁徙,识别、绘制和保护这些迁徙走廊已成为地方、区域和全球保护工作的重点。布朗桥运动模型(BBMMs)通常用于根据经验识别这些季节性迁徙走廊;然而,这些模型需要以相对频繁的间隔采样位置数据,才能获得动物迁徙路径的可靠估计值。将 BBMM 拟合到稀疏的位置数据违反了连续位置之间有条件随机移动的假设,因此在创建个体和种群水平的出现分布时会高估迁徙走廊的面积(和宽度),并且在绘制迁徙走廊地图时无法使用低频率或稀疏的数据。为了扩大 BBMMs 的应用范围,将稀疏 GPS 数据也纳入其中,我们提出了另一种方法,利用稀疏 GPS 数据建立迁徙走廊模型。我们使用从怀俄明州和爱达荷州的四个骡鹿群(Odocoileus hemionus)和四个麋鹿群(Cervus canadensis)收集的每 2 小时一次的 GPS 数据演示了这种方法。首先,我们使用 BBMM 估算 2 小时数据的基线走廊。然后,我们对 2 小时数据进行子采样,每 12 小时采样一个位置(代表数据稀疏),并使用固定运动方差(FMV)值对 12 小时数据进行 BBMM 拟合,而不是根据经验估计布朗运动方差。对一系列 FMV 值进行了测试,以确定最接近基线迁徙走廊的值。在特定物种范围内的FMV值(骡鹿:400-1200平方米;麋鹿:600-1600平方米)成功地划定了与2小时基线走廊相似的迁徙走廊;总体而言,较低的FMV值划定的走廊较窄,较高的FMV值划定的走廊较宽。800平方米(骡鹿)和1000平方米(麋鹿)的最佳FMV值与传统BBMM的2小时走廊相比,减少了12小时走廊的膨胀。因此,这种 FMV 方法可以利用稀少的迁徙数据来接近真实的迁徙走廊尺寸,在迁徙数据收集不频繁的情况下提供了一种重要的替代方法。这种方法大大增加了可用于绘制迁徙走廊图的数据集数量,是全球管理和保护的有用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Estimating ungulate migration corridors from sparse movement data

Estimating ungulate migration corridors from sparse movement data

Many ungulates migrate between distinct summer and winter ranges, and identifying, mapping, and conserving these migration corridors have become a focus of local, regional, and global conservation efforts. Brownian bridge movement models (BBMMs) are commonly used to empirically identify these seasonal migration corridors; however, they require location data sampled at relatively frequent intervals to obtain a robust estimate of an animal's movement path. Fitting BBMMs to sparse location data violates the assumption of conditional random movement between successive locations, overestimating the area (and width) of a migration corridor when creating individual and population-level occurrence distributions and precluding the use of low-frequency, or sparse, data in mapping migration corridors. In an effort to expand the utility of BBMMs to include sparse GPS data, we propose an alternative approach to model migration corridors from sparse GPS data. We demonstrate this method using GPS data collected every 2 h from four mule deer (Odocoileus hemionus) and four elk (Cervus canadensis) herds within Wyoming and Idaho. First, we used BBMMs to estimate a baseline corridor for the 2-h data. We then subsampled the 2-h data to one location every 12 h (a proxy for sparse data) and fitted BBMMs to the 12-h data using a fixed motion variance (FMV) value, instead of estimating the Brownian motion variance empirically. A range of FMV values was tested to identify the value that best approximated the baseline migration corridor. FMV values within a species-specific range (mule deer: 400–1200 m2; elk: 600–1600 m2) successfully delineated migration corridors similar to the 2-h baseline corridors; overall, lower values delineated narrower corridors and higher values delineated wider corridors. Optimal FMV values of 800 m2 (mule deer) and 1000 m2 (elk) decreased the inflation of the 12-h corridors relative to the 2-h corridors from traditional BBMMs. This FMV approach thus enables using sparse movement data to approximate realistic migration corridor dimensions, providing an important alternative when movement data are collected infrequently. This approach greatly expands the number of datasets that can be used for migration corridor mapping—a useful tool for management and conservation across the globe.

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来源期刊
Ecosphere
Ecosphere ECOLOGY-
CiteScore
4.70
自引率
3.70%
发文量
378
审稿时长
15 weeks
期刊介绍: The scope of Ecosphere is as broad as the science of ecology itself. The journal welcomes submissions from all sub-disciplines of ecological science, as well as interdisciplinary studies relating to ecology. The journal''s goal is to provide a rapid-publication, online-only, open-access alternative to ESA''s other journals, while maintaining the rigorous standards of peer review for which ESA publications are renowned.
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