Trends in plant cover derived from vegetation plot data using ordinal zero-augmented beta regression

IF 2.2 3区环境科学与生态学 Q2 ECOLOGY

Journal of Vegetation Science Pub Date : 2024-08-23 DOI:10.1111/jvs.13295

Arco J. van Strien, Kathryn M. Irvine, Cas Retel

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引用次数: 0

Abstract

Questions

Plant cover values in vegetation plot data are bounded between 0 and 1, and cover is typically recorded in discrete classes with non-equal intervals. Consequently, cover data are skewed and heteroskedastic, which hampers the application of conventional regression methods. Recently developed ordinal beta regression models consider these statistical difficulties. Our primary question is whether we can detect species trends in vegetation plot time series data with this modelling approach. A second question is whether trends in cover have additional value compared to trends in occurrence, which are easier to assess for practitioners.

Location

The Netherlands, Western Europe.

Methods

We used vegetation plot data collected from 10,000 fixed plots which were surveyed once every four years during 1999–2022. We used the ordinal zero-augmented beta regression (OZAB) model, a hierarchical model consisting of a logistic regression for presence and an ordinal beta regression for cover. We adapted the OZAB model for longitudinal data and produced estimates of cover and occurrence for each four-year period. Thereafter we assessed trends in cover and in occurrence across all periods.

Results

We found evidence of a trend in cover in 318 out of the 721 species (44%) with sufficient data. Most species showed similar directional trends in occurrence and percent cover. No trend in occurrence was detected for 64 species that had evidence of a trend in cover. Declining species had stronger relative changes in cover than in occurrence.

Conclusions

Our model enables researchers to detect trends in cover using longitudinal vegetation plot data. Cover trends often corroborated trends in occurrence, but we also regularly found trends in cover even in the absence of evidence for trends in occurrence. Our approach thus contributes to a more complete picture of (changes in) vegetation composition based on large monitoring data sets.

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利用序数零增量贝塔回归从植被小区数据中得出的植物覆盖趋势

问题植被小区数据中的植物覆盖度值介于 0 和 1 之间，覆盖度通常以不等距的离散等级记录。因此，植被覆盖度数据是偏斜和异方差的，这妨碍了传统回归方法的应用。最近开发的顺序贝塔回归模型考虑到了这些统计困难。我们的首要问题是，能否利用这种建模方法检测植被地块时间序列数据中的物种趋势。第二个问题是，与发生趋势相比，植被覆盖率的趋势是否具有额外的价值，因为发生趋势更容易为实践者所评估。地点荷兰，西欧。方法我们使用了 1999-2022 年间每四年调查一次的 10,000 个固定地块的植被数据。我们使用了序数零增量贝塔回归（OZAB）模型，这是一个分层模型，由表示存在的逻辑回归和表示覆盖的序数贝塔回归组成。我们对 OZAB 模型进行了调整，使其适用于纵向数据，并得出了每四年的覆盖率和出现率估计值。之后，我们评估了覆盖率和出现率在各个时期的变化趋势。结果我们发现，在数据充足的 721 个物种中，有 318 个物种（44%）的覆盖率呈上升趋势。大多数物种的出现率和覆盖率都呈现出类似的方向性趋势。在有证据表明覆盖率呈趋势的 64 个物种中，未发现出现趋势。正在减少的物种在覆盖率方面的相对变化比出现率方面的变化要大。结论我们的模型使研究人员能够利用纵向植被小区数据检测覆盖度的变化趋势。覆盖度的变化趋势往往与出现率的变化趋势相吻合，但即使没有证据表明出现率的变化趋势，我们也能经常发现覆盖度的变化趋势。因此，我们的方法有助于在大型监测数据集的基础上更全面地了解植被组成的（变化）情况。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Vegetation Science 环境科学-林学

CiteScore

6.00

自引率

3.60%

发文量

审稿时长

2 months

期刊介绍： The Journal of Vegetation Science publishes papers on all aspects of plant community ecology, with particular emphasis on papers that develop new concepts or methods, test theory, identify general patterns, or that are otherwise likely to interest a broad international readership. Papers may focus on any aspect of vegetation science, e.g. community structure (including community assembly and plant functional types), biodiversity (including species richness and composition), spatial patterns (including plant geography and landscape ecology), temporal changes (including demography, community dynamics and palaeoecology) and processes (including ecophysiology), provided the focus is on increasing our understanding of plant communities. The Journal publishes papers on the ecology of a single species only if it plays a key role in structuring plant communities. Papers that apply ecological concepts, theories and methods to the vegetation management, conservation and restoration, and papers on vegetation survey should be directed to our associate journal, Applied Vegetation Science journal.