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Results obtained by projecting backward the plane where the y–z axis is located in Figure 3. The figure reveals different levels of fluctuation in α, β, and γ diversities around 2005. Before and after 2005, diversity levels change with varying years of monitoring, but around the exclosure year 2005, species diversity remains at a stable level. This indicates that the exclosure year 2005 is optimal for maintaining species diversity and ecosystem health.
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Plant species diversity has long been a focal point in ecological studies. In order to study the changes in species diversity at different spatial scales (α, β, and γ diversities) in the restoration process of grassland vegetation in fragile desert steps, this study took desert steppe of Inner Mongolia as the research object and employed a two‐fact...
Citations
... Huang at el [18] established that in desert grasslands, how long the creatures were exclosed impacted on plant species diversity. Two key insights were offered from their study: the duration of these environmental manipulations results in longterm effects and the importance of temporal dynamics when designing ecological experiments. ...
Designing experiments and analyzing them statistically are essential for accuracy, reliablity and reproducibility of research in biological sciences. This thesis looks at best practices and pitfalls for experimental methodologies, though in particular the application of Bayesian inference and machine learning, and data integration techniques. Four typical advanced algorithms were applied to analyze biological dataset, including Bayesian Hierarchical Modelling, Random Forest Classification, Principal Component Analysis (PCA), and Support Vector Machines (SVM). These results showed that Bayesian Hierarchical Modeling had 92.5% accuracy to predict experimental outcomes and that Random Forest surpassed the traditional methods with classification accuracy of 89.3%. This computational efficiency comes at the expense of only 78.6% of the information being lost during the process of data dimensionality reduction, when comparing known fractions of information related to those of the other techniques. A complex biological pattern recognition is achieved with an 87.1% accuracy using SVM. The advantages of using AI and probabilistic models in the experimental biology were demonstrated and compared with the results from the existing studies. In addition, animal welfare and replicability were improved, as part of this work. The findings underscore the importance of integrating state of the art statistical models, interdisciplinary thinking and computational techniques to increase the reproducibility and impact of biological science. It offers a framework for optimizing experimental design, data analysing strategies, and statistical biases mitigating to more robust and moral research processes.
The effect of grazing intensity on plant diversity has been widely studied. In this study, desert steppes with different grazing intensities (no grazing (CK), light grazing (LG), moderate grazing (MG), heavy grazing (HG), and extremely heavy grazing (EG)) in Inner Mongolia were selected to study the changes in species diversity at different spatial scales (α, β, and γ diversity) and the α diversity of different plant groups (dominant species, common species, and rare species).The results showed that the α, β, and γ diversity first decreased and then increased with increasing grazing intensity, and β diversity was observed to be the most sensitive index to the grazing intensity. Grazing had the greatest impact on the α diversity of rare species and the least impact on the α diversity of common species. Therefore, rare species are of great significance for the maintenance and assessment of biodiversity. We concluded that grazing leads to a sensitive response of β diversity, and this sensitive phenomenon is mainly affected by rare species. The results could provide scientific bases for the protection of plant diversity and sustainable grazing in desert steppes.